Merge remote-tracking branch 'origin/tl'

src/main/java/com/aps/entity/Algorithm/Chromosome.java

@@ -32,6 +32,11 @@ public class Chromosome {
     /// </summary>
     private List<Integer> machineSelection;
 
+    // 缓存标记
+    private transient boolean machineStrDirty = true;
+    private transient boolean operationStrDirty = true;
+    private transient boolean geneStrDirty = true;
+
     /// <summary>
     /// 工序排序部分（工件/订单ID）
     /// </summary>
@@ -43,7 +48,10 @@ public class Chromosome {
     private String geneStr;
 
     public String  getGeneStr() {
-        geneStr=getMachineStr()+"_"+getOperationStr();
+        if (geneStrDirty || machineStrDirty || operationStrDirty) {
+            geneStr = getMachineStr() + "_" + getOperationStr();
+            geneStrDirty = false;
+        }
         return geneStr;
     }
 
@@ -51,26 +59,44 @@ public class Chromosome {
 
 
     public String  getMachineStr() {
-
-        machineStr=   Optional.ofNullable(machineSelection)
+        if (machineStrDirty) {
+            machineStr = Optional.ofNullable(machineSelection)
                     .orElse(Collections.emptyList())
                     .stream()
-                .map(String::valueOf) // Integer转String
+                    .map(String::valueOf)
                     .collect(Collectors.joining(","));
+            machineStrDirty = false;
+            geneStrDirty = true;
+        }
         return machineStr;
     }
 
     public String  getOperationStr() {
-
-
-        operationStr=   Optional.ofNullable(operationSequencing)
+        if (operationStrDirty) {
+            operationStr = Optional.ofNullable(operationSequencing)
                     .orElse(Collections.emptyList())
                     .stream()
-                .map(String::valueOf) // Integer转String
+                    .map(String::valueOf)
                     .collect(Collectors.joining(","));
+            operationStrDirty = false;
+            geneStrDirty = true;
+        }
         return operationStr;
     }
 
+    // 重写 setter 方法，在设置时标记为脏
+    public void setMachineSelection(List<Integer> machineSelection) {
+        this.machineSelection = machineSelection;
+        this.machineStrDirty = true;
+        this.geneStrDirty = true;
+    }
+
+    public void setOperationSequencing(List<Integer> operationSequencing) {
+        this.operationSequencing = operationSequencing;
+        this.operationStrDirty = true;
+        this.geneStrDirty = true;
+    }
+
 
 
 

src/main/java/com/aps/entity/Algorithm/ScheduleParams.java

@@ -15,7 +15,7 @@ public class ScheduleParams {
 
     // 基础参数（自适应调整的基准值）
     private static final int MIN_POPULATION_SIZE = 10;
-    private static final int MAX_POPULATION_SIZE = 50;
+    private static final int MAX_POPULATION_SIZE = 20;
     private static final int MIN_MAX_ITERATIONS = 50;
     private static final int MAX_MAX_ITERATIONS = 100;
     private static final float MIN_CROSSOVER_PROB = 0.6f;
@@ -172,10 +172,14 @@ public class ScheduleParams {
         // 1. 种群大小：50 ~ 500，随工序数线性增加
         populationSize = (int) Math.max(MIN_POPULATION_SIZE,
                 Math.min(MAX_POPULATION_SIZE, MIN_POPULATION_SIZE + totalOps * populationSizeCoeff));
+
+      int maxthead=  Runtime.getRuntime().availableProcessors() - 1;
+        populationSize= populationSize / maxthead*maxthead;
+
         // 确保偶数（方便交叉）
-        if (populationSize % 2 != 0) {
-            populationSize += 1;
-        }
+//        if (populationSize % 2 != 0) {
+//            populationSize += 1;
+//        }
 
         // 2. 最大迭代次数：50 ~ 200，随工序数线性减少
         maxIterations = (int) Math.max(MIN_MAX_ITERATIONS,

src/main/java/com/aps/entity/basic/GlobalParam.java

@@ -73,11 +73,11 @@ public class GlobalParam {
     public GlobalParam() {
         // 初始化默认目标值配置
         //完工时间、总流程时间、总换型时间、机器负载标准差、延迟时间
-        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_MAKESPAN, true, 1, 0.6));
-        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_FLOW_TIME, false, 2, 0.1));
+        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_MAKESPAN, true, 1, 0.3));
+        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_FLOW_TIME, true, 2, 0.1));
         objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_SETUP_TIME, true, 2, 0.1));
-        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_MACHINE_LOAD, false, 3, 0.1));
-        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_TARDINESS, true, 1, 0.3));
+        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_MACHINE_LOAD, true, 1, 0.1));
+        objectiveConfigs.add(new ObjectiveConfig(OBJECTIVE_TARDINESS, true, 1, 0.6));
 
         objectiveConfigs.sort(Comparator.comparing((ObjectiveConfig op) -> op.getLevel()));
     }

src/main/java/com/aps/entity/basic/KpiTargetConfig.java

+package com.aps.entity.basic;
+
+/**
+ * 作者：佟礼
+ * 时间：2026-04-09
+ */
+public class KpiTargetConfig {
+}

src/main/java/com/aps/entity/basic/Order.java

@@ -157,6 +157,11 @@ public class Order {
      */
     private boolean newCreate = false;
 
+    /**
+     * 是否新创建半成品，默认值：false
+     */
+    private boolean newSfCreate = false;
+    
     /**
      * 是否创建BOM，默认值：false
      */

src/main/java/com/aps/service/Algorithm/FitnessCalculator.java

@@ -21,8 +21,10 @@ public class FitnessCalculator {
 
         ArrayList<Double> list = new ArrayList<>();
         double fitness=0;
+        double total=0;
         int i=0;
         int level=-1;
+       double[] weightedObjectives=new double[chromosome.getObjectives().length];
         for (ObjectiveConfig config : param.getObjectiveConfigs()) {
             if( config.isEnabled()) {
 
@@ -65,7 +67,9 @@ public class FitnessCalculator {
                 if (!param.isPureNSGAIIMode()) {
                     val = val * config.getWeight();
                 }
+                weightedObjectives[i]=val;
                 fitness += val;
+                total+=val;
                 i++;
             }
         }
@@ -87,6 +91,8 @@ public class FitnessCalculator {
 //            fitness ≈ 0.01
 
         // 适应度值（越大越好）
+        chromosome.setFitness(total);
+        chromosome.setWeightedObjectives(weightedObjectives);
         return list.stream().mapToDouble(Double::doubleValue).toArray();
     }
 
@@ -147,7 +153,7 @@ public class FitnessCalculator {
         // 2. 计算每个个体与平均值的平方差之和
         float sumSqDiff = 0.0f;
         for (Chromosome chromosome : population) {
-            double diff = chromosome.getWeightedObjective() - avgFitness;
+            double diff = chromosome.getFitness() - avgFitness;
             sumSqDiff += diff * diff;
         }
 
@@ -155,6 +161,19 @@ public class FitnessCalculator {
         return (double) Math.sqrt(sumSqDiff / popSize);
     }
 
+    /**
+     * 比较两个染色体的优劣（基于fitnessLevel多层次比较）
+     */
+    public boolean isBetter(Chromosome c1, Chromosome c2) {
+        for (int i = 0; i < c1.getFitnessLevel().length; i++) {
+            double[] Fitness1 = c1.getFitnessLevel();
+            double[] Fitness2 = c2.getFitnessLevel();
+            if (Fitness1[i] > Fitness2[i]) {
+                return true;
+            }
+        }
+        return false;
+    }
 
 
 

src/main/java/com/aps/service/Algorithm/GeneticAlgorithm.java

@@ -425,7 +425,7 @@ public class GeneticAlgorithm {
 //            chromosome.setResultOld(new CopyOnWriteArrayList<>());
 //        }
 
-        decoder.decodeChromosomeWithCache(chromosome);
+        decoder.decodeChromosomeWithCache(chromosome,false);
         if (chromosome.getFitness() == 0) {
             chromosome.setFitness(_fitnessCalculator.calculateFitness(chromosome, _objectiveWeights));
         }

src/main/java/com/aps/service/Algorithm/GeneticDecoder.java

@@ -88,17 +88,17 @@ public class GeneticDecoder {
     {
         FileHelper.writeLogFile("解码---------------"+population.size() );
 
-        boolean ismore=false;
+        boolean ismore=true;
         if(ismore) {
             CompletableFuture.allOf(population.stream()
-                            .map(chromosome -> CompletableFuture.runAsync(() -> decodeChromosomeWithCache(chromosome), decodeExecutor))
+                            .map(chromosome -> CompletableFuture.runAsync(() -> decodeChromosomeWithCache(chromosome,false), decodeExecutor))
                             .toArray(CompletableFuture[]::new))
                     .join();
 
         } else  {
             if (population != null && population.size() > 0) {
                 population.forEach(chromosome -> {
-                    decodeChromosomeWithCache( chromosome);
+                    decodeChromosomeWithCache( chromosome,false);
                 });
             }
         }
@@ -176,7 +176,7 @@ public class GeneticDecoder {
 //        return  chromosome;
 //    }
 
-    public Chromosome decodeChromosomeWithCache(Chromosome chromosome) {
+    public Chromosome decodeChromosomeWithCache(Chromosome chromosome,boolean isParallel) {
         String cacheKey = createCacheKey(chromosome);
         // FileHelper.writeLogFile("解码-----------开始-------"+chromosome.getID()+":"+cacheKey );
 
@@ -214,7 +214,7 @@ public class GeneticDecoder {
             return chromosome;
         }
         // 3. 缓存未命中：执行解码逻辑
-        decode(chromosome);
+        decode(chromosome,isParallel);
 
         // 4. 将解码结果存入缓存（无锁，ConcurrentHashMap 线程安全）
         decodingCache.putIfAbsent(cacheKey, chromosome);
@@ -266,7 +266,7 @@ public class GeneticDecoder {
         Initialization initialization=new Initialization();
         initialization.generateInitialSFPopulation(chromosome,globalOpList);
         chromosome.getGlobalOpList().addAll(globalOpList);
-return 1;
+        return 1;
     }
 
     private void   CreateNewOpSequence(Chromosome chromosome)
@@ -279,10 +279,10 @@ return 1;
                 .distinct()
                 .collect(Collectors.toList());
 
-if(finishedOrder==null||finishedOrder.size()==0)
-{
+        if(finishedOrder==null||finishedOrder.size()==0)
+        {
             return;
-}
+        }
 
         List<Integer> oldSequence=chromosome.getOperationSequencing();
         List<Integer> finalSequence = new ArrayList<>();
@@ -367,12 +367,11 @@ if(finishedOrder==null||finishedOrder.size()==0)
         orderProcessCounter.put(orderid, scheduledCount);
     }
 
-    public void decode(Chromosome chromosome) {
-        int operationCount = chromosome.getAllOperations().size();
-        int cpuCores = Runtime.getRuntime().availableProcessors();
+    public void decode(Chromosome chromosome,boolean isParallel) {
+
 //
 //        // 根据工序数量和CPU核心数决定是否使用内部并行
-        if (operationCount > 5000 && cpuCores > 4) {
+        if (isParallel) {
             // 使用设备并行处理
             //  FileHelper.writeLogFile("使用并行处理 _s");
             parallelDecodeByMachine(chromosome);
@@ -392,7 +391,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
     public void serialDecode(Chromosome chromosome) {
 
 
-       // List<OrderMaterialRequirement> orderMaterials = materialRequirementService.buildMultiLevelRequirementNetwork(chromosome, sceneId, baseTime,_globalParam);
+        List<OrderMaterialRequirement> orderMaterials = materialRequirementService.buildMultiLevelRequirementNetwork(chromosome, sceneId, baseTime,_globalParam);
         chromosome.setScenarioID(sceneId);
         if(_globalParam.isIsCheckSf()) {
             int isnew=   generateGlobalOpList(chromosome);
@@ -473,12 +472,15 @@ if(finishedOrder==null||finishedOrder.size()==0)
         Map<Integer, Integer> orderLastEndTime = new HashMap<>();
         Map<Integer, Entry> entryIndexById = new HashMap<>();
         Map<Integer, List<Entry>> entrysBygroupId = new HashMap<>();
+
+        Map<Integer, GAScheduleResult> scheduleIndexById = new ConcurrentHashMap<>();
         for (Entry op : allOperations) {
             int groupId = op.getGroupId();
             orderProcessCounter.putIfAbsent(groupId, 0);
             orderLastEndTime.putIfAbsent(groupId, 0);
             entryIndexById.put(op.getId(),op);
             entrysBygroupId.computeIfAbsent(groupId, k -> new ArrayList<>()).add(op);
+            //scheduleIndexById.put(op.getId(),null);
         }
 
 //        Map<Long, String> machineState = chromosome.getMachines().stream()
@@ -487,9 +489,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
         //  List<Entry> allScheduledOps = new ArrayList<>();
         // 缓存机器任务
         Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache = new HashMap<>();
-        for (Entry op : allOperations) {
 
-        }
         for (int groupId : chromosome.getOperationSequencing()) {
             int scheduledCount = orderProcessCounter.get(groupId);
             List<Entry> orderOps = entrysBygroupId.get(groupId).stream()
@@ -555,7 +555,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
             //  Machine machine = machineIdMap.get(machineId);
             // int changeoverTime =0; //(lastDiscreteParameter.isEmpty() ||
             // lastDiscreteParameter.equals(currentOp.getDiscreteParameter())) ? 0 : 0;
-            int actualEndTime =  processOperation(currentOp,machineId,processTime,machineOption,chromosome,machineIdMap,machineTasksCache);
+            int actualEndTime =  processOperation(currentOp,machineId,processTime,machineOption,chromosome,machineIdMap,machineTasksCache,entryIndexById,scheduleIndexById);
             //  int actualEndTime = processWithSingleMachine(currentOp, machine, processTime, prevtime,machineOption, chromosome,false,prevendtime,machineTasksCache);
 
             orderProcessCounter.put(groupId, orderProcessCounter.get(groupId) + 1);
@@ -570,131 +570,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
         calculateScheduleResult(chromosome);
     }
 
-    public void parallelDecode(Chromosome chromosome) {
-        FileHelper.writeLogFile("构建工序依赖图 _s");
-        // 1. 构建工序依赖图
-        Map<Integer, List<Integer>> dependencyGraph = buildDependencyGraph(chromosome);
-        FileHelper.writeLogFile("拓扑排序 _s");
-        // 2. 拓扑排序
-        List<Integer> topologicalOrder = topologicalSort(dependencyGraph,chromosome);
-        FileHelper.writeLogFile("按设备分组 _s");
-        // 3. 按设备分组
-        Map<Long, List<Integer>> machineToOpsMap = new HashMap<>();
-        Map<Integer, Integer> opToGroupIdMap = new HashMap<>();
-        Map<Integer, Long> opToMachineMap = new HashMap<>();
-        List<GlobalOperationInfo> globalOpList=chromosome.getGlobalOpList();
-        // 步骤1：生成“工序→机器/加工时间”映射
-        List<OpMachine> opMachineMap = new ArrayList<>();
-        Map<String, OpMachine> opMachineKeyMap = new HashMap<>();
-        Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache = new HashMap<>();
-        Map<Long, Machine> machineIdMap = chromosome.getMachines().stream()
-                .collect(Collectors.toMap(Machine::getId, m -> m));
-        // 构建工序ID索引：opId -> Entry（O(1)查找）
-        Map<Integer, Entry> opIdIndex = buildOpIdIndex(chromosome);
-
-        for (GlobalOperationInfo globalOp : globalOpList) {
-            int globalOpId = globalOp.getGlobalOpId();
-            Entry op = globalOp.getOp();
-            int groupId = globalOp.getGroupId();
-            int sequence = globalOp.getSequence();
 
-            // 从MachineSelection中获取当前工序的机器选择顺序号
-            int machineSeq = chromosome.getMachineSelection().get(globalOpId);
-            List<MachineOption> optionalMachines = op.getMachineOptions();
-
-            // 验证：机器顺序号必须在可选范围内
-            if (machineSeq < 1 || machineSeq > optionalMachines.size()) {
-                throw new IllegalStateException(String.format(
-                        "全局工序%d（订单%d工序%d）的机器顺序号%d超出范围（1-%d）",
-                        globalOpId, groupId, sequence, machineSeq, optionalMachines.size()));
-            }
-
-            // 获取选择的设备ID和加工时间
-            MachineOption selectedMachine = optionalMachines.get(machineSeq - 1);
-            OpMachine opMachine=new OpMachine();
-            opMachine.setGroupId(groupId);
-            opMachine.setSequence(sequence);
-            opMachine.setMachineId(selectedMachine.getMachineId());
-            opMachine.setProcessingTime(selectedMachine.getProcessingTime());
-            opMachine.setRuntime(selectedMachine.getRuntime());
-            opMachine.setSingleOut(selectedMachine.getSingleOut());
-
-            opMachineMap.add(opMachine);
-            String key = opMachine.getGroupId() + "_" + opMachine.getSequence();
-            opMachineKeyMap.put(key, opMachine);
-        }
-        FileHelper.writeLogFile("预处理工序信息 _s");
-        // 预处理工序信息
-        for (int opId : topologicalOrder) {
-           // Entry op = findOperationById(chromosome, opId);
-            Entry op = opIdIndex.get(opId);
-            if (op != null) {
-                // 假设已经为工序分配了设备
-
-                String opMachineKey = op.getGroupId() + "_" + op.getSequence();
-                OpMachine machineOption = opMachineKeyMap.get(opMachineKey);
-                if (machineOption != null) {
-                    machineToOpsMap.computeIfAbsent(machineOption.getMachineId(), k -> new ArrayList<>()).add(opId);
-                    opToGroupIdMap.put(opId, op.getGroupId());
-                    opToMachineMap.put(opId, machineOption.getMachineId());
-                }
-            }
-        }
-
-        // 4. 并行处理
-        Map<Integer, CompletableFuture<Void>> opFutures = new HashMap<>();
-        Map<Long, CompletableFuture<Void>> machineFutures = new HashMap<>();
-        FileHelper.writeLogFile("并行处理 _s");
-        for (int opId : topologicalOrder) {
-            // 获取当前工序的依赖
-           // Entry op = findOperationById(chromosome, opId);
-            Entry op = opIdIndex.get(opId);
-            List<Integer> dependencies = dependencyGraph.getOrDefault(opId, Collections.emptyList());
-
-            // 等待所有依赖完成
-            List<CompletableFuture<Void>> dependencyFutures = dependencies.stream()
-                    .map(opFutures::get)
-                    .filter(Objects::nonNull)
-                    .collect(Collectors.toList());
-
-            String opMachineKey = op.getGroupId() + "_" + op.getSequence();
-            OpMachine machineOption = opMachineKeyMap.get(opMachineKey);
-
-            Long machineId = machineOption.getMachineId();
-            double processTime = machineOption.getProcessingTime();
-
-            // 等待当前设备的上一个工序完成
-
-            CompletableFuture<Void> machineFuture = machineFutures.getOrDefault(machineId, CompletableFuture.completedFuture(null));
-
-            // 合并所有依赖
-            List<CompletableFuture<Void>> allDependencies = new ArrayList<>(dependencyFutures);
-            allDependencies.add(machineFuture);
-
-            // 创建当前工序的处理任务
-            CompletableFuture<Void> currentFuture = CompletableFuture.allOf(allDependencies.toArray(new CompletableFuture[0]))
-                    .thenRunAsync(() -> {
-                        // 处理当前工序
-                        int actualEndTime =  processOperation(op,machineId,processTime,machineOption,chromosome,machineIdMap,machineTasksCache);
-
-                    });
-
-            // 保存当前工序的Future
-            opFutures.put(opId, currentFuture);
-            // 更新设备的Future
-            machineFutures.put(machineId, currentFuture);
-        }
-
-        // 等待所有工序完成
-        CompletableFuture.allOf(opFutures.values().toArray(new CompletableFuture[0])).join();
-
-        FileHelper.writeLogFile("calculateScheduleResult _s");
-        if(chromosome.getReOrderids()!=null&&chromosome.getReOrderids().size()>0) {
-            chromosome.getOperationSequencing().removeIf(t -> chromosome.getReOrderids().contains(t));
-        }
-
-        calculateScheduleResult(chromosome);
-    }
     public void parallelDecodeByMachine(Chromosome chromosome) {
         // 1. 获取工序处理顺序
         List<Integer> operationSequencing = chromosome.getOperationSequencing();
@@ -748,9 +624,15 @@ if(finishedOrder==null||finishedOrder.size()==0)
         // 初始化订单处理计数器
         Map<Integer, Integer> orderProcessCounter = new HashMap<>();
 
+        Map<Integer, Entry> entryIndexById = new HashMap<>();
+        Map<Integer, List<Entry>> entrysBygroupId = new HashMap<>();
+        Map<Integer, GAScheduleResult> scheduleIndexById= new ConcurrentHashMap<>();
         for (Entry op : allOperations) {
             int groupId = op.getGroupId();
             orderProcessCounter.putIfAbsent(groupId, 0);
+
+            entryIndexById.put(op.getId(),op);
+            entrysBygroupId.computeIfAbsent(groupId, k -> new ArrayList<>()).add(op);
         }
         Map<Long, List<Entry>> machineToOpsMap = new HashMap<>();
         //按照 OperationSequencing 顺序生成 machineToOpsMap
@@ -886,11 +768,11 @@ if(finishedOrder==null||finishedOrder.size()==0)
             // 工序超过500的设备直接在主线程串行处理
             if (ops.size() > 500) {
                 //   FileHelper.writeLogFile("设备 " + machineId + " 有 " + ops.size() + " 个工序，直接串行处理");
-                processMachineOps(chromosome, machineId, ops, dependencyGraph, opCompleted, lock, opMachineKeyMap, machineIdMap, machineTasksCache);
+                processMachineOps(chromosome, machineId, ops, dependencyGraph, opCompleted, lock, opMachineKeyMap, machineIdMap, machineTasksCache, entryIndexById,scheduleIndexById);
             } else {
                 // 工序少的设备创建线程处理
                 Thread thread = new Thread(() -> {
-                    processMachineOps(chromosome, machineId, ops, dependencyGraph, opCompleted, lock, opMachineKeyMap, machineIdMap, machineTasksCache);
+                    processMachineOps(chromosome, machineId, ops, dependencyGraph, opCompleted, lock, opMachineKeyMap, machineIdMap, machineTasksCache, entryIndexById,scheduleIndexById);
                 });
                 thread.start();
                 threads.add(thread);
@@ -920,7 +802,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
      */
     private void processMachineOps(Chromosome chromosome, Long machineId, List<Entry> ops,
                                    Map<Integer, List<Integer>> dependencyGraph,
-                                   Map<Integer, Boolean> opCompleted, Object lock,Map<String, OpMachine> opMachineKeyMap,Map<Long, Machine> machineIdMap,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache) {
+                                   Map<Integer, Boolean> opCompleted, Object lock,Map<String, OpMachine> opMachineKeyMap,Map<Long, Machine> machineIdMap,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById) {
         //long machineStartTime = System.currentTimeMillis();
 
         for (int i = 0; i < ops.size(); i++) {
@@ -956,7 +838,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
 
             // 处理当前工序
             //   long processStartTime = System.currentTimeMillis();
-            int actualEndTime =  processOperation(op,machineId,processTime,machineOption,chromosome,machineIdMap,machineTasksCache);
+            int actualEndTime =  processOperation(op,machineId,processTime,machineOption,chromosome,machineIdMap,machineTasksCache, entryIndexById,scheduleIndexById);
 
             //   long processEndTime = System.currentTimeMillis();
 
@@ -1114,7 +996,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
 
 
 
-    private  int processOperation(Entry currentOp,Long machineId,double processTime,OpMachine machineOption,Chromosome chromosome,Map<Long, Machine> machineIdMap,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache)
+    private  int processOperation(Entry currentOp,Long machineId,double processTime,OpMachine machineOption,Chromosome chromosome,Map<Long, Machine> machineIdMap,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById)
     {
 
         Machine targetMachine = machineIdMap.get(machineId);
@@ -1123,7 +1005,7 @@ if(finishedOrder==null||finishedOrder.size()==0)
         int teardownTime = currentOp.getTeardownTime();
         if (!currentOp.getPrevEntryIds().isEmpty()) {
             // 处理多个前工序
-            prevtime= CalPrevtime( prevtime, currentOp, chromosome, processTime, targetMachine);
+            prevtime= CalPrevtime( prevtime, currentOp, chromosome, processTime, targetMachine,entryIndexById, scheduleIndexById);
         }
 
         int bomtime = getOperationBOMTime(currentOp,chromosome);
@@ -1131,14 +1013,14 @@ if(finishedOrder==null||finishedOrder.size()==0)
         int prevendtime=prevtime;
         prevtime = Math.max(prevtime, bomtime);
         Machine machine = machineIdMap.get(machineId);
-        int actualEndTime = processWithSingleMachine(currentOp, machine, processTime, prevtime,machineOption, chromosome,false,prevendtime,machineTasksCache);
-return actualEndTime;
+        int actualEndTime = processWithSingleMachine(currentOp, machine, processTime, prevtime,machineOption, chromosome,false,prevendtime,machineTasksCache,entryIndexById, scheduleIndexById);
+        return actualEndTime;
     }
 
 
     private int processWithSingleMachine(Entry operation, Machine machine, double  processingTime,
 
-                                         int prevOperationEndTime,OpMachine  machineOption, Chromosome chromosome,boolean calbom,int prevendtime,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache) {
+                                         int prevOperationEndTime,OpMachine  machineOption, Chromosome chromosome,boolean calbom,int prevendtime,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById) {
         int  processingTimeTotal=0;
         int earliestStartTime = prevOperationEndTime;
         if(operation.getConstTime()==1)//常数时间
@@ -1239,7 +1121,7 @@ return actualEndTime;
                         : earliestStartTime;
 
             }
-             bomtime=  EditOperationBOMTime(operation,chromosome,earliestStartTime,machineTasksCache);
+            bomtime=  EditOperationBOMTime(operation,chromosome,earliestStartTime,machineTasksCache, entryIndexById, scheduleIndexById);
 
 
             if(bomtime>prevendtime&&bomtime<earliestStartTimeold)
@@ -1394,7 +1276,7 @@ return actualEndTime;
         if (machineTasks != null) {
             machineTasks.add(result);
         }
-
+        scheduleIndexById.put(operation.getId(),result);
         return endTime;
     }
 
@@ -1405,16 +1287,18 @@ return actualEndTime;
      * @param chromosome
      * @return
      */
-    private  int CalPrevtime(int prevtime,Entry currentOp,Chromosome chromosome,double processTime,Machine machine) {
+    private  int CalPrevtime(int prevtime,Entry currentOp,Chromosome chromosome,double processTime,Machine machine,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById) {
         List<OperationDependency> FsOperations = currentOp.getPrevEntryIds().stream()
                 .filter(t -> t.getDependencyType() == DependencyType.FinishToStart)
                 .collect(Collectors.toList());//串行
         if (FsOperations != null && FsOperations.size() > 0) {
             for (OperationDependency opid : FsOperations) {
-                List<GAScheduleResult> prevOperations = chromosome.getResult().stream()
-                        .filter(t -> t.getGroupId() == currentOp.getGroupId() && t.getOperationId() == opid.getPrevOperationId())
-                        .collect(Collectors.toList());//多台
-                for (GAScheduleResult prevOp : prevOperations) {
+//                List<GAScheduleResult> prevOperations = chromosome.getResult().stream()
+//                        .filter(t ->  t.getOperationId() == opid.getPrevOperationId())
+//                        .collect(Collectors.toList());//
+                GAScheduleResult prevOp= scheduleIndexById.get(opid.getPrevOperationId());
+                // for (GAScheduleResult prevOp : prevOperations) {
+                if(prevOp!=null){
                     //加上后处理时间
                     prevtime = Math.max(prevtime, prevOp.getEndTime() + currentOp.getTeardownTime());
                 }
@@ -1431,10 +1315,13 @@ return actualEndTime;
             for (int j=0;j<SSOperations.size();j++)
             {
                 OperationDependency opid=SSOperations.get(j);
-                List<GAScheduleResult> prevOperations = chromosome.getResult().stream()
-                        .filter(t -> t.getGroupId() == currentOp.getGroupId() && t.getOperationId() == opid.getPrevOperationId())
-                        .collect(Collectors.toList());//多台
-                for (GAScheduleResult prevOp : prevOperations) {
+//                List<GAScheduleResult> prevOperations = chromosome.getResult().stream()
+//                        .filter(t ->  t.getOperationId() == opid.getPrevOperationId())
+//                        .collect(Collectors.toList());//多台
+                GAScheduleResult prevOp= scheduleIndexById.get(opid.getPrevOperationId());
+
+                // for (GAScheduleResult prevOp : prevOperations) {
+                if(prevOp!=null){
                     newScheduleResult.add(prevOp);
                     newScheduleResultDetails.addAll(prevOp.getGeneDetails());
                 }
@@ -1444,15 +1331,17 @@ return actualEndTime;
                     prevtime = Math.max(prevtime, prevtime1);
 
                 }else {
-                    Entry entry=  chromosome.getAllOperations().stream()
-                            .filter(t->t.getId()==opid.getPrevOperationId())
-                            .findFirst().orElse(null);
+//                    Entry entry=  chromosome.getAllOperations().stream()
+//                            .filter(t->t.getId()==opid.getPrevOperationId())
+//                            .findFirst().orElse(null);
+                    Entry entry=entryIndexById.get(opid.getPrevOperationId());
                     OperationDependency opid2=SSOperations.get(j+1);
-                    Entry entry1=  chromosome.getAllOperations().stream()
-                            .filter(t->t.getId()==opid2.getPrevOperationId())
-                            .findFirst().orElse(null);
-
-                    if(entry.getRoutingDetailId()!=entry1.getRoutingDetailId())
+//                    Entry entry1=  chromosome.getAllOperations().stream()
+//                            .filter(t->t.getId()==opid2.getPrevOperationId())
+//                            .findFirst().orElse(null);
+                    Entry entry1=entryIndexById.get(opid2.getPrevOperationId());
+//不是同一前工序，效率不能合并   newScheduleResult 清空,如何是一个工序就合并计算
+                    if(entry!=null&&entry1!=null&&entry.getRoutingDetailId()!=entry1.getRoutingDetailId())
                     {
 
                         int prevtime1 = CalSSPrevtime(newScheduleResult,newScheduleResultDetails, prevtime, currentOp, chromosome, processTime, machine);
@@ -1577,7 +1466,7 @@ return actualEndTime;
         return Math.max(rawTime, sfTime);
     }
 
-    private int EditOperationBOMTime(Entry currentOp, Chromosome chromosome,int earliestStartTime,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache) {
+    private int EditOperationBOMTime(Entry currentOp, Chromosome chromosome,int earliestStartTime,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById) {
 
 
         List<OrderMaterialRequirement> opboms=  currentOp.getMaterialRequirements();
@@ -1585,13 +1474,13 @@ return actualEndTime;
         {
             return  0;
         }
-LocalDateTime earliestStartTime1=baseTime.plusSeconds(earliestStartTime);
-        materialRequirementService.EditOperationBOM(currentOp,chromosome,earliestStartTime1,this,machineTasksCache);
-return getOperationBOMTime(currentOp, chromosome);
+        LocalDateTime earliestStartTime1=baseTime.plusSeconds(earliestStartTime);
+        materialRequirementService.EditOperationBOM(currentOp,chromosome,earliestStartTime1,this,machineTasksCache, entryIndexById, scheduleIndexById, baseTime);
+        return getOperationBOMTime(currentOp, chromosome);
 
     }
 
-    public void EditorderOperation(Chromosome chromosome, int groupId,double needed,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache){
+    public void EditorderOperation(Chromosome chromosome, int groupId,double needed,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById){
         List<Entry> orderOps = chromosome.getAllOperations().stream()
                 .filter(t -> t.getGroupId() == groupId)
                 .sorted(Comparator.comparing(Entry::getSequence))
@@ -1635,7 +1524,7 @@ return getOperationBOMTime(currentOp, chromosome);
             int teardownTime = currentOp.getTeardownTime();
             if (!currentOp.getPrevEntryIds().isEmpty()) {
                 // 处理多个前工序
-                prevtime= CalPrevtime( prevtime, currentOp, chromosome, processTime, targetMachine);
+                prevtime= CalPrevtime( prevtime, currentOp, chromosome, processTime, targetMachine, entryIndexById,scheduleIndexById);
             }
 
             // 上个离散参数
@@ -1650,7 +1539,7 @@ return getOperationBOMTime(currentOp, chromosome);
                     .findFirst()
                     .orElse(null);
 // 缓存机器任务
-            int actualEndTime = processWithSingleMachine(currentOp, machine, processTime, prevtime,opMachine, chromosome,true,prevendtime,machineTasksCache);
+            int actualEndTime = processWithSingleMachine(currentOp, machine, processTime, prevtime,opMachine, chromosome,true,prevendtime,machineTasksCache, entryIndexById, scheduleIndexById);
 
 
         }
@@ -1687,10 +1576,10 @@ return getOperationBOMTime(currentOp, chromosome);
                             && t.getProductOrderID() != null && !t.getProductOrderID().isEmpty()
                     )
                     .collect(Collectors.toList());
-if(MaterialRequirements==null||MaterialRequirements.size()==0)
-{
+            if(MaterialRequirements==null||MaterialRequirements.size()==0)
+            {
                 continue;
-}
+            }
             for (OrderMaterialRequirement orderMaterial:MaterialRequirements) {
                 List<Integer> orderids= orderMaterial.getProductOrderID();
                 if(orderids!=null&&orderids.size()>0) {
@@ -2443,7 +2332,7 @@ if(MaterialRequirements==null||MaterialRequirements.size()==0)
                                 .filter(t -> orderIds.contains(t.getOrderId()))
                                 .max(Comparator.comparing(Order::getDueDate))
                                 .orElse(null);
-                        if (order.isNewCreate()) {
+                        if (order.isNewSfCreate()) {
                             continue;
                         }
                         LocalDateTime dueDateTime = order.getDueDate();

src/main/java/com/aps/service/Algorithm/GeneticOperations.java

@@ -25,6 +25,10 @@ public class GeneticOperations {
 
     private static ScheduleParams param;
 
+    public GeneticOperations() {
+
+    }
+
     public GeneticOperations(GlobalParam globalParam,List<Entry> _allOperations,ScheduleParams _param) {
         _GlobalParam=globalParam;
         allOperations = _allOperations;
@@ -424,10 +428,12 @@ public class GeneticOperations {
     public void DelOrder(Chromosome chromosome) {
         List<Entry> allOperations = chromosome.getAllOperations();
         List<GlobalOperationInfo> globalOpList=  chromosome.getGlobalOpList();
+        if(chromosome.getOrders()==null||chromosome.getOrders().size()==0)
+            return;
         List<Order> orders = chromosome.getOrders();
         List<Integer> OperationSequencing=  chromosome.getOperationSequencing();
         List<Integer> newoorderids= orders.stream()
-                .filter(t->t.isNewCreate())
+                .filter(t->t.isNewSfCreate())
                 .map(Order::getId)
                 .sorted(Comparator.reverseOrder())
                 .collect(Collectors.toList());

src/main/java/com/aps/service/Algorithm/HillClimbing.java

 package com.aps.service.Algorithm;
 
 import com.aps.common.util.FileHelper;
+import com.aps.common.util.GlobalCacheUtil;
 import com.aps.common.util.ProductionDeepCopyUtil;
 import com.aps.entity.Algorithm.*;
 import com.aps.entity.Algorithm.IDAndChildID.GroupResult;
@@ -21,6 +22,11 @@ public class HillClimbing {
 
     private List<Entry> allOperations;
 
+    private  List<Order> orders;
+    private  List<Material> materials;
+
+    private  List<GroupResult> _entryRel;
+
     //按优先级分组工序
     private Map<Double, List<Entry>> priorityGroups;
 
@@ -30,10 +36,17 @@ public class HillClimbing {
     // 从List转为Map op.getGroupId() + "_" + op.getSequence(); 做主键
     private Map<String, Entry> entrys;
 
-    public HillClimbing(List<Entry> allOperations) {
+    private FitnessCalculator fitnessCalculator;
+
+    public HillClimbing(List<Entry> allOperations, List<Order> orders,
+                        List<Material> materials,List<GroupResult> entryRel,   FitnessCalculator _fitnessCalculator) {
 
         this.allOperations = allOperations;
+        this.orders = orders;
+        this.materials = materials;
 
+        _entryRel=entryRel;
+        fitnessCalculator=_fitnessCalculator;
         // 按优先级分组工序
         priorityGroups = groupOperationsByPriority();
 
@@ -92,7 +105,7 @@ public class HillClimbing {
 
                     Chromosome machineChange = generateMachineChange(current, op.getMachineOptions(), maPos);
                     if (machineChange != null) {
-                        decode(decoder, machineChange);
+                        decode(decoder, machineChange,machines);
                         if (isBetter(machineChange, current)) {
                             current = machineChange;
                             if (isBetter(current, best)) {
@@ -263,7 +276,7 @@ public class HillClimbing {
 
                     Chromosome machineChange = generateMachineChange(current, op.getMachineOptions(), maPos);
                     if (machineChange != null) {
-                        decode(decoder, machineChange);
+                        decode(decoder, machineChange,machines);
                         if (isBetter(machineChange, current)) {
                             current = machineChange;
                             if (isBetter(current, best)) {
@@ -328,7 +341,7 @@ public class HillClimbing {
                     }
 
                     Chromosome newChromosome = swapAtDistance(current, opPos, -s * step);
-                    decode(decoder, newChromosome);
+                    decode(decoder, newChromosome,machines);
 
                     if (isBetter(newChromosome, current)) {
                         current = newChromosome;
@@ -357,7 +370,7 @@ public class HillClimbing {
                     }
 
                     Chromosome newChromosome = swapAtDistance(current, opPos, s * step);
-                    decode(decoder, newChromosome);
+                    decode(decoder, newChromosome,machines);
 
                     if (isBetter(newChromosome, current)) {
                         current = newChromosome;
@@ -411,21 +424,24 @@ public class HillClimbing {
     }
 
 
-
-    /**
-     * 构建位置索引：groupId_sequence -> position
-     */
-    private Map<String, Integer> buildPositionIndex(Chromosome chromosome) {
-
-        String fitness="";
+    private  void  writeKpi(Chromosome chromosome) {
+        String fitness = "";
         double[] fitness1 = chromosome.getFitnessLevel();
         for (int i = 0; i < fitness1.length; i++) {
 
-            fitness+=fitness1[i]+",";
+            fitness += fitness1[i] + ",";
         }
 
         FileHelper.writeLogFile(String.format("爬山法 - kpi：%s", fitness));
 
+    }
+    /**
+     * 构建位置索引：groupId_sequence -> position
+     */
+    private Map<String, Integer> buildPositionIndex(Chromosome chromosome) {
+
+        writeKpi(chromosome);
+
 
         Map<String, Integer> index = new HashMap<>();
         List<Integer> os = chromosome.getOperationSequencing();
@@ -546,114 +562,6 @@ public class HillClimbing {
         }
         return neighbor;
     }
-
-    /**
-     * 生成邻域解
-     */
-    private List<Chromosome> generateNeighbors(Chromosome chromosome) {
-        List<Chromosome> neighbors = new ArrayList<>();
-
-        // 生成交换邻域
-        neighbors.add(generateSwapNeighbor(chromosome));
-
-        // 生成反转邻域
-        neighbors.add(generateReverseNeighbor(chromosome));
-
-        // 生成插入邻域
-        neighbors.add(generateInsertNeighbor(chromosome));
-
-        // 生成机器选择邻域
-        neighbors.add(generateMachineChangeNeighbor(chromosome));
-
-        return neighbors;
-    }
-
-    /**
-     * 生成交换邻域解
-     */
-    private Chromosome generateSwapNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int idx1 = rnd.nextInt(os.size());
-            int idx2 = rnd.nextInt(os.size());
-            while (idx2 == idx1) {
-                idx2 = rnd.nextInt(os.size());
-            }
-            java.util.Collections.swap(os, idx1, idx2);
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
-    }
-
-    /**
-     * 生成反转邻域解
-     */
-    private Chromosome generateReverseNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int start = rnd.nextInt(os.size());
-            int end = rnd.nextInt(os.size());
-            while (end <= start) {
-                end = rnd.nextInt(os.size());
-            }
-            java.util.Collections.reverse(os.subList(start, end + 1));
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
-    }
-
-    /**
-     * 生成插入邻域解
-     */
-    private Chromosome generateInsertNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int idx1 = rnd.nextInt(os.size());
-            int idx2 = rnd.nextInt(os.size());
-            while (idx2 == idx1) {
-                idx2 = rnd.nextInt(os.size());
-            }
-            int value = os.remove(idx1);
-            os.add(idx2, value);
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
-    }
-
-    /**
-     * 生成机器选择邻域解
-     */
-    private Chromosome generateMachineChangeNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> ms = neighbor.getMachineSelection();
-        List<GlobalOperationInfo>  globalOpList=chromosome.getGlobalOpList();
-        if (!ms.isEmpty()) {
-            int idx = rnd.nextInt(ms.size());
-            GlobalOperationInfo globalOp = globalOpList.get(idx);
-            Entry op = globalOp.getOp();
-
-            if (op.getMachineOptions().size() > 1) {
-                int currentMachineSeq = ms.get(idx);
-                List<Integer> availableMachines = new ArrayList<>();
-                for (int i = 1; i <= op.getMachineOptions().size(); i++) {
-                    if (i != currentMachineSeq) {
-                        availableMachines.add(i);
-                    }
-                }
-
-                if (!availableMachines.isEmpty()) {
-                    int newMachineSeq = availableMachines.get(rnd.nextInt(availableMachines.size()));
-                    ms.set(idx, newMachineSeq);
-                    neighbor.setMachineSelection(ms);
-                }
-            }
-        }
-        return neighbor;
-    }
-
     /**
      * 生成机器选择邻域解
      */
@@ -703,11 +611,34 @@ public class HillClimbing {
     /**
      * 解码染色体
      */
-    private void decode(GeneticDecoder decoder, Chromosome chromosome) {
-        // chromosome.setResult(new java.util.concurrent.CopyOnWriteArrayList<>());
-        decoder.decodeChromosomeWithCache(chromosome);
+    private void decode(GeneticDecoder decoder, Chromosome chromosome , List<Machine> machines) {
+        chromosome.setResult(new CopyOnWriteArrayList<>());
+
+        // 假设Machine类有拷贝方法，或使用MapStruct等工具进行映射
 
+        chromosome.setMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setOrders(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(orders), Order.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setOperatRel(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(_entryRel), GroupResult.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setMaterials(ProductionDeepCopyUtil.deepCopyList(materials,Material.class)); // 简单拷贝，实际可能需要深拷贝
+
+        chromosome.setAllOperations(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(allOperations), Entry.class) ); // 简单拷贝，实际可能需要深拷贝
+        //chromosome.setObjectiveWeights(_objectiveWeights);
+        //  chromosome.setBaseTime(param.getBaseTime());
+        //  chromosome.setInitMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        //   _sceneService.saveChromosomeToFile(chromosome, "12345679");
+
+        // 加载锁定工单到ResultOld
+        List<GAScheduleResult> lockedOrders = GlobalCacheUtil.get("locked_orders_" + chromosome.getScenarioID());
+        if (lockedOrders != null && !lockedOrders.isEmpty()) {
+            chromosome.setResultOld(ProductionDeepCopyUtil.deepCopyList(lockedOrders, GAScheduleResult.class));
+            FileHelper.writeLogFile("将 " + lockedOrders.size() + " 个锁定工单加载到初始种群中");
+        } else {
+            chromosome.setResultOld(new CopyOnWriteArrayList<>());
         }
+        decoder.decodeChromosomeWithCache(chromosome,false);
+
+    }
+
 
     /**
      * 比较两个染色体的优劣

src/main/java/com/aps/service/Algorithm/HybridAlgorithm.java

 package com.aps.service.Algorithm;
 
-        import com.aps.common.util.DateTimeUtil;
-        import com.aps.common.util.DeepCopyUtil;
-        import com.aps.common.util.FileHelper;
-        import com.aps.common.util.ProductionDeepCopyUtil;
-        import com.aps.entity.Algorithm.*;
+import com.aps.common.util.*;
+import com.aps.entity.Algorithm.*;
         import com.aps.entity.Algorithm.IDAndChildID.GroupResult;
         import com.aps.entity.basic.*;
         import com.aps.service.plan.MachineSchedulerService;
@@ -55,11 +52,11 @@ public class HybridAlgorithm {
     // 初始化算法实例
     private HillClimbing _hillClimbing;
     private SimulatedAnnealing _simulatedAnnealing;
-    private ParallelLocalSearch _parallelLocalSearch;
+
 
     // 初始化算法实例
     private TabuSearch _tabuSearch;
-    private TabuSearchWithSA _tabuSearchWithSA;
+
 
 
     public HybridAlgorithm(GlobalParam globalParam,List<Machine> machines, List<Order> orders,
@@ -109,18 +106,16 @@ int opcount=allOperations.size();
         // 预生成全局工序列表（所有初始化方法共享同一顺序）
         List<GlobalOperationInfo> globalOpList = initialization.generateGlobalOpList();
         // 初始化变邻域搜索
-        _vns = new VariableNeighborhoodSearch(_GlobalParam, allOperations, globalOpList, _fitnessCalculator, _objectiveWeights);
-        _hillClimbing = new HillClimbing(allOperations);
-        _simulatedAnnealing = new SimulatedAnnealing( allOperations);
-        _parallelLocalSearch = new ParallelLocalSearch(_GlobalParam, allOperations, globalOpList, _fitnessCalculator, _objectiveWeights);
-        _tabuSearch = new TabuSearch(_GlobalParam, allOperations, globalOpList, _fitnessCalculator, _objectiveWeights);
-        _tabuSearchWithSA = new TabuSearchWithSA(_GlobalParam, allOperations, globalOpList, _fitnessCalculator, _objectiveWeights);
+        _vns = new VariableNeighborhoodSearch( allOperations,orders,materials,_entryRel,  _fitnessCalculator );
+        _hillClimbing = new HillClimbing(allOperations,orders,materials,_entryRel,  _fitnessCalculator);
+        _simulatedAnnealing = new SimulatedAnnealing( allOperations,orders,materials,_entryRel,  _fitnessCalculator);
+        _tabuSearch = new TabuSearch(allOperations,orders,materials,_entryRel,  _fitnessCalculator);
         FileHelper.writeLogFile("初始化种群-----------开始-------");
 
 
         // 步骤1：使用构造启发式算法生成初始种群
         FileHelper.writeLogFile("构造启发式初始化-----------开始-------");
-        List<Chromosome> population = initialization.generateHeuristicInitialPopulation(param, globalOpList);
+        List<Chromosome> population = initialization.generateHeuristicInitialPopulation(param);
         FileHelper.writeLogFile("构造启发式初始化-----------结束-------");
 
 
@@ -138,15 +133,15 @@ int opcount=allOperations.size();
         FileHelper.writeLogFile("按Objectives去重-----------开始-------" + population.size());
         population = chromosomeDistinctByObjectives(population);
         FileHelper.writeLogFile("按Objectives去重-----------结束-------" + population.size());
-
-
+      //  if(1==1)
+      //  return getBestChromosome(population.get(0), param.getBaseTime(), starttime);
         // 步骤2：对初始种群进行爬山法局部优化
 
         if(opcount<20 ) {
             FileHelper.writeLogFile("爬山法局部优化-----------开始-------");
             GeneticDecoder hillClimbingDecoder = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
 
-            Chromosome optimized = _hillClimbing.BatchSearchAll(population,machines, hillClimbingDecoder);
+            Chromosome optimized = _hillClimbing.search(population.get(0), hillClimbingDecoder,machines);
             FileHelper.writeLogFile("爬山法局部优化-----------结束-------");
             return getBestChromosome(optimized, param.getBaseTime(), starttime);
         }
@@ -156,27 +151,37 @@ int opcount=allOperations.size();
         // 步骤2：对初始种群进行模拟退火+爬山法优化
 
         GeneticDecoder saDecoder1 = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
+
+        GeneticDecoder vnsDecoder1 = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
+
+        GeneticDecoder tabuDecoder1 = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
+
+
         if(opcount<800 ) {
             FileHelper.writeLogFile("模拟退火+爬山法优化-----------开始-------");
-            Chromosome saHcOptimized = _simulatedAnnealing.batchSearchGetMax(population, saDecoder1, machines);
+            Chromosome saHcOptimized = _simulatedAnnealing.searchWithHillClimbing(population.get(0),_vns, saDecoder1, machines);
             FileHelper.writeLogFile("模拟退火+爬山法优化-----------结束-------");
 
             return getBestChromosome(saHcOptimized, param.getBaseTime(), starttime);
         }
+        if(opcount>=800 ) {
+            Chromosome best=population.get(0);
+            best = _simulatedAnnealing.search(best, _tabuSearch, _vns, saDecoder1, machines);
+            best = _vns.search(best, vnsDecoder1, machines);
+            best = _tabuSearch.search(best, _vns, tabuDecoder1, machines);
+
+            return getBestChromosome(best, param.getBaseTime(), starttime);
+
+        }
 
 
-        List<List<Chromosome>> combinedFronts = _nsgaIIUtils.parallelFastNonDominatedSort(population);
+
+
+        Chromosome best=ProductionDeepCopyUtil.deepCopy(population.get(0), Chromosome.class); // GetBest(combinedFronts,"初始");
         int ordercount = globalOpList.stream()
                 .mapToInt(GlobalOperationInfo::getGroupId)
                 .max()
                 .orElse(0);
-        Chromosome best=GetBest(combinedFronts,"初始");
-
-//        best=fronts.get(0).stream()
-//                .max(Comparator.comparingDouble(Chromosome::getFitness))
-//                .orElse(null);
-
-        double bestFitness=best.getFitness();
 
         if(population.size()<param.getTournamentSize())
         {
@@ -185,7 +190,8 @@ int opcount=allOperations.size();
         }
 
 
-        int Iteration=0;
+        int noImproveCount = 0;
+        int maxNoImprove = 10;
         // 步骤2：迭代进化
         FileHelper.writeLogFile("迭代进化-----------开始-------"+param.getMaxIterations());
         for (int iter = 0; iter < param.getMaxIterations(); iter++) {
@@ -193,6 +199,7 @@ int opcount=allOperations.size();
             FileHelper.writeLogFile("迭代进化------"+iter+"-----开始-------");
 
 
+
             // 计算种群适应度标准差，微调参数
             double fitnessStd = _fitnessCalculator.calculateFitnessStd(population);
             param.fineTuneParams(iter, fitnessStd);
@@ -204,141 +211,62 @@ int opcount=allOperations.size();
             }
             FileHelper.writeLogFile("选择操作-----------开始-------");
 
-            // 选择前移除适应度为 0 或明显劣于当前最优解的个体，减少选择池规模：
-            double minValidFitness = bestFitness * 0.5; // 保留最优解50%以上的个体
-            List<Chromosome> validPopulation = population.stream()
-                    .filter(c -> c.getFitness() >= minValidFitness)
-                    .collect(Collectors.toList());
-
             // 选择操作
-            List<Chromosome> selected = geneticOps.tournamentSelection(validPopulation);
+            List<Chromosome> selected = geneticOps.tournamentSelection(population);
             FileHelper.writeLogFile("选择操作-----------结束-------");
 
-            // 变邻域搜索
-            FileHelper.writeLogFile("变邻域搜索-----------开始-------");
-            GeneticDecoder vnsDecoder = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
-            List<Chromosome> vnsImproved = _vns.search(selected, vnsDecoder, param);
-            selected.addAll(vnsImproved);
-            FileHelper.writeLogFile("变邻域搜索-----------结束-------");
-
-            // 模拟退火全局搜索
-            FileHelper.writeLogFile("模拟退火搜索-----------开始-------");
-            GeneticDecoder saDecoder = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
-            List<Chromosome> saImproved = new ArrayList<>();
-            for (Chromosome chromosome : selected) {
-              // Chromosome saOptimized = _simulatedAnnealing.search(chromosome, saDecoder, param);
-             //   saImproved.add(saOptimized);
-            }
-            selected.addAll(saImproved);
-            FileHelper.writeLogFile("模拟退火搜索-----------结束-------");
-
-
-            // 多种局部搜索算法并行
-            FileHelper.writeLogFile("并行局部搜索-----------开始-------");
-            GeneticDecoder parallelDecoder = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
-            List<Chromosome> parallelImproved = new ArrayList<>();
-            for (Chromosome chromosome : selected) {
-                Chromosome parallelOptimized = _parallelLocalSearch.search(chromosome, parallelDecoder, param);
-                parallelImproved.add(parallelOptimized);
-            }
-            selected.addAll(parallelImproved);
-            FileHelper.writeLogFile("并行局部搜索-----------结束-------");
-
-            // 禁忌搜索+模拟退火
-            FileHelper.writeLogFile("禁忌搜索+模拟退火-----------开始-------");
-            GeneticDecoder tsDecoder = new GeneticDecoder(_GlobalParam, param.getBaseTime(), machines, orders, materials, machineScheduler, materialRequirementService, sceneId);
-            List<Chromosome> tsImproved = new ArrayList<>();
-            for (Chromosome chromosome : selected) {
-                Chromosome tsOptimized = _tabuSearchWithSA.search(chromosome, tsDecoder, param);
-                tsImproved.add(tsOptimized);
-            }
-            selected.addAll(tsImproved);
-            FileHelper.writeLogFile("禁忌搜索+模拟退火-----------结束-------");
 
             // 交叉操作
             FileHelper.writeLogFile("交叉操作-----------开始-------");
 
-            List<Chromosome> nextPopulation = new ArrayList<>();
-            for (int i = 0; i < selected.size(); i += 2) {
-                if (i + 1 >= selected.size()) {
-                    selected.get(i).setID(UUID.randomUUID().toString());
-                    nextPopulation.add(selected.get(i));
-                    break;
-                }
-
-                Chromosome parent1 = selected.get(i);
-                Chromosome parent2 = selected.get(i + 1);
-                if (rnd.nextDouble() < param.getCrossoverProb()) {
-                    // 假设PoxCrossover返回包含两个子染色体的数组
-                    Pair<Chromosome, Chromosome> children = geneticOps.poxCrossover(parent1, parent2, ordercount);
-                    nextPopulation.add(children.getFirst());
-                    nextPopulation.add(children.getSecond());
-                } else {
-                    nextPopulation.add(parent1);
-                    nextPopulation.add(parent2);
-                }
-            }
-            FileHelper.writeLogFile("交叉操作-----------结束-------");
-            FileHelper.writeLogFile("变异操作-----------开始-------");
-            // 变异操作
-            List<Chromosome> nextPopulation1 = new ArrayList<>();
-            for (Chromosome chromosome : nextPopulation) {
-                if (rnd.nextDouble() < param.getMutationProb()) {
-                    Chromosome chromosome1=  ProductionDeepCopyUtil.deepCopy(chromosome,Chromosome.class);
-                    chromosome1.setID(UUID.randomUUID().toString());
-                    geneticOps.mutate(chromosome1, globalOpList);
-                    nextPopulation1.add(chromosome1);
-                }
-            }
-            nextPopulation.addAll(nextPopulation1);
-            FileHelper.writeLogFile("变异操作-----------结束-------");
-            FileHelper.writeLogFile("变异批量解码-----------开始-------");
-
-
+            List<Chromosome> newPopulation = new ArrayList<>();
+            newPopulation.add(best);
+            int n=0;
+            while (newPopulation.size() < param.getPopulationSize()) {
+                // 选择父代
+                FileHelper.writeLogFile("交叉-----------开始-------"+n);
+
+                Chromosome parent1 = selected.get(rnd.nextInt(selected.size()));
+                Chromosome parent2 = selected.get(rnd.nextInt(selected.size()));
+                // 交叉
+                Chromosome child = parent1;
+//                if (rnd.nextDouble() < param.getCrossoverProb()) {
+//                    // 假设PoxCrossover返回包含两个子染色体的数组
+//                    Pair<Chromosome, Chromosome> children = geneticOps.poxCrossover(parent1, parent2, ordercount);
+//                    child=children.getFirst();
+//                    initDataToChromosome(child,param, allOperations,globalOpList);
+//                    child.setID(UUID.randomUUID().toString());
+//                }
+//                // 变异
+//                if (rnd.nextDouble() < param.getMutationProb()) {
+//                    geneticOps.mutate(child, globalOpList);
+//                }
+                // 核心融合链（工业级标准顺序：GA生成子代 → SA跳坑 → VNS扩邻域 → TS精优化）
+                child = _simulatedAnnealing.search(child,_tabuSearch,_vns, saDecoder1, machines);
+                child = _vns.search(child, vnsDecoder1, machines);
+               child = _tabuSearch.search(child, _vns,tabuDecoder1, machines);
 
-            Chromosomedecode(param,allOperations,globalOpList,nextPopulation);
-            FileHelper.writeLogFile("变异批量解码-----------结束-------");
-//            // 精英保留
-//            List<Chromosome> population1 = population.stream()
-//                    .sorted((c1, c2) -> Double.compare(c2.getFitness(), c1.getFitness()))
-//                    .collect(Collectors.toList()); // 降序排序
-//
-//
-//            List<Chromosome>   elites = population1.subList(0, param.getElitismCount());
+                newPopulation.add(child);
 
-            List<Chromosome> newPopulation = new ArrayList<>();
-            //保留一定数量的上一代
-            List<Chromosome>  populationcopy = _nsgaIIUtils.selectNextPopulation(combinedFronts, param.getPopulationSize()/5);
-
-            newPopulation.addAll(populationcopy);
-            newPopulation.addAll(nextPopulation);
-            newPopulation= chromosomeDistinct1(newPopulation);
-            FileHelper.writeLogFile("非支配排序-----------开始-------");
-            // 2.7 非支配排序
-            combinedFronts = _nsgaIIUtils.parallelFastNonDominatedSort(newPopulation);
-            FileHelper.writeLogFile("非支配排序-----------结束-------");
-            // 2.8 选择下一代种群
-            population = _nsgaIIUtils.selectNextPopulation(combinedFronts, param.getPopulationSize());
-            // 更新种群
-//            population = newPopulation.stream()
-//                    .limit(param.getPopulationSize() )
-//                    .collect(Collectors.toList());
+                FileHelper.writeLogFile("交叉-----------结束-------"+n);
 
+            }
+            population= chromosomeDistinctByObjectives(newPopulation);;
 
-            best= GetBest(combinedFronts,String.valueOf(iter));
-            if(bestFitness<best.getFitness())
+            if(_fitnessCalculator.isBetter(population.get(0),best))
             {
-                bestFitness=best.getFitness();
-                Iteration=1;
+                best=ProductionDeepCopyUtil.deepCopy(population.get(0), Chromosome.class); // GetBest(combinedFronts,"初始");
+
+                noImproveCount=1;
             }else {
 
-                Iteration++;
-                if (Iteration > 5) {
+                noImproveCount++;
+                if (noImproveCount > 5) {
                     // 当连续 5 代无最优解提升时，降低交叉概率（如从 0.8 降至 0.5）、提高变异概率（如从 0.1 升至 0.2）
                     param.fineTuneParams();
                 }
             }
-            if(Iteration>10)
+            if(noImproveCount>maxNoImprove)
             {
                 break;
             }
@@ -379,18 +307,7 @@ int opcount=allOperations.size();
         return best;
     }
 
-    private Chromosome GetBest(List<List<Chromosome>> fronts,String msg)
-    {
-        // WriteKpis(fronts,msg);
-        List<Chromosome> fChromosomes= fronts.get(0);
-        fChromosomes.sort((c1, c2) -> {
-            int rankCompare = Double.compare(c1.getCrowdingDistance(), c2.getCrowdingDistance());
-            return rankCompare != 0 ? rankCompare : Double.compare(c1.getFitness(), c2.getFitness());
-        });
-        Chromosome best=fChromosomes.get(0);
-        //  WriteKpi(best,"最大");
-        return best;
-    }
+
     private List<Chromosome> chromosomeDistinct(List<Chromosome> population)
     {
         if(population==null)
@@ -413,7 +330,7 @@ int opcount=allOperations.size();
     }
 
     /**
-     * 按Objectives去重，相同目标值的方案只保留一个（保留fitness最大的）
+     * 按Objectives去重
      */
     private List<Chromosome> chromosomeDistinctByObjectives(List<Chromosome> population)
     {
@@ -440,34 +357,36 @@ int opcount=allOperations.size();
             }
         }
 
-        return new ArrayList<>(objectivesMap.values());
+        // 按 fitnessLevel 倒序排序（多层次比较）
+        List<Chromosome> result = new ArrayList<>(objectivesMap.values());
+        result.sort((c1, c2) -> {
+            if (isBetter(c1, c2)) {
+                return -1; // c1 比 c2 好，c1 应该排在前面
+            } else if (isBetter(c2, c1)) {
+                return 1; // c2 比 c1 好，c2 应该排在前面
+            } else {
+                return 0; // 两者相等
             }
+        });
 
-    private List<Chromosome> chromosomeDistinct1(List<Chromosome> population)
-    {
-        if(population==null)
-        {
-            FileHelper.writeLogFile(String.format("排产-----------方案数量---%d-------",0 ));
-            return population;
+        return result;
+    }
+    /**
+     * 比较两个染色体的优劣（基于fitnessLevel多层次比较）
+     */
+    private boolean isBetter(Chromosome c1, Chromosome c2) {
+        for (int i = 0; i < c1.getFitnessLevel().length; i++) {
+            double[] Fitness1 = c1.getFitnessLevel();
+            double[] Fitness2 = c2.getFitnessLevel();
+            if (Fitness1[i] > Fitness2[i]) {
+                return true;
+            }
+        }
+        return false;
     }
 
 
 
-        // List<Chromosome>  population1 = population.stream().filter(t->t.getGeneStr()==null).collect(Collectors.toList());
-
-        population = population.stream()
-                .collect(Collectors.toMap(
-                        Chromosome::getGeneStr, // key：去重的字段（GeneStr）
-                        u -> u,        // value：Chromosome对象
-                        (u1, u2) -> u1.getFitness() > u2.getFitness() ? u1 : u2 // 重复时保留第一个元素
-                ))
-                .values() // 获取去重后的
-                .stream()
-                .collect(Collectors.toList());
-
-        return  population;
-    }
-
     private void  WriteKpis(List<List<Chromosome>> fronts,String index) {
         for (int i=0;i<fronts.size();i++)
         {
@@ -496,6 +415,40 @@ int opcount=allOperations.size();
             new ThreadPoolExecutor.CallerRunsPolicy() // 任务满了主线程执行，不丢失任务
     );
 
+    private  void  initDataToChromosome(Chromosome chromosome,ScheduleParams param, List<Entry> allOperations,List<GlobalOperationInfo> globalOpList) {
+
+        if (chromosome==null){
+            System.out.println("chromosome==null");
+            return;
+        }
+
+        chromosome.setResult(new CopyOnWriteArrayList<>());
+
+        // 假设Machine类有拷贝方法，或使用MapStruct等工具进行映射
+
+        chromosome.setMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setOrders(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(orders), Order.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setOperatRel(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(_entryRel), GroupResult.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setMaterials(ProductionDeepCopyUtil.deepCopyList(materials,Material.class)); // 简单拷贝，实际可能需要深拷贝
+
+        chromosome.setAllOperations(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(allOperations), Entry.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setGlobalOpList(ProductionDeepCopyUtil.deepCopyList(globalOpList, GlobalOperationInfo.class) ); // 简单拷贝，实际可能需要深拷贝
+        //chromosome.setObjectiveWeights(_objectiveWeights);
+        chromosome.setBaseTime(param.getBaseTime());
+        //  chromosome.setInitMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        //   _sceneService.saveChromosomeToFile(chromosome, "12345679");
+
+        // 加载锁定工单到ResultOld
+        List<GAScheduleResult> lockedOrders = GlobalCacheUtil.get("locked_orders_" + sceneId);
+        if (lockedOrders != null && !lockedOrders.isEmpty()) {
+            chromosome.setResultOld(ProductionDeepCopyUtil.deepCopyList(lockedOrders, GAScheduleResult.class));
+            FileHelper.writeLogFile("将 " + lockedOrders.size() + " 个锁定工单加载到初始种群中");
+        } else {
+            chromosome.setResultOld(new CopyOnWriteArrayList<>());
+        }
+    }
+
+
     private  void  Chromosomedecode(ScheduleParams param, List<Entry> allOperations,List<GlobalOperationInfo> globalOpList,List<Chromosome> population)
     {
         FileHelper.writeLogFile("解码---------------"+population.size() );
@@ -542,7 +495,7 @@ int opcount=allOperations.size();
         //  chromosome.setInitMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
         //   _sceneService.saveChromosomeToFile(chromosome, "12345679");
 
-        decoder.decodeChromosomeWithCache(chromosome);
+        decoder.decodeChromosomeWithCache(chromosome,false);
 //        if (chromosome.getFitness() == 0) {
 //            chromosome.setFitness(_fitnessCalculator.calculateFitness(chromosome, _objectiveWeights));
 //        }

src/main/java/com/aps/service/Algorithm/Initialization.java

@@ -343,7 +343,7 @@ chromo.setOrders(new CopyOnWriteArrayList<>(orders));
     /**
      * 使用构造启发式算法生成初始种群
      */
-    public List<Chromosome> generateHeuristicInitialPopulation(ScheduleParams param, List<GlobalOperationInfo> globalOpList) {
+    public List<Chromosome> generateHeuristicInitialPopulation(ScheduleParams param) {
         List<Chromosome> population = new ArrayList<>();
         int populationSize = param.getPopulationSize();
         this.baseTime=param.getBaseTime();

src/main/java/com/aps/service/Algorithm/MaterialRequirementService.java

@@ -179,7 +179,7 @@ public class MaterialRequirementService {
                                     double allneeded = component.getSpentQty().doubleValue()/ component.getMainQty().doubleValue() * operation.getQuantity();
 
                                     OrderMaterialRequirement orderMaterialRequirement = CreateMaterialRequirement(material, orderId, "", operation,
-                                            allneeded,component.getSpentQty().doubleValue(), component.getMainQty().doubleValue(),operation.getQuantity());
+                                            allneeded,component.getSpentQty().doubleValue(), component.getMainQty().doubleValue(),operation.getQuantity(),baseTime);
                                     orderMaterialRequirement.setBomId(component.getStrId());
                                     if (!material.getMaterialTypeName().equals("MP") && isIsCheckSf) {
                                         materialIds.add(materialId);//半成品ID
@@ -503,8 +503,6 @@ public class MaterialRequirementService {
      * @return 所有物料需求列表
      */
     public List<OrderMaterialRequirement> buildMultiLevelRequirementNetwork(Chromosome chromosome,String sceneId, LocalDateTime _baseTime,GlobalParam _globalParam) {
-        baseTime=_baseTime;
-        globalParam=_globalParam;
         List<OrderMaterialRequirement> allRequirements = new ArrayList<>();
         List<Order> childorders = new ArrayList<>();
         List<Entry> _newEntrys = new ArrayList<>();
@@ -519,7 +517,7 @@ public class MaterialRequirementService {
                 .entrySet().stream()
                 .collect(Collectors.toMap(Map.Entry::getKey, e -> 0));
 
-        List<Integer> operationSequencing=chromosome.getOperationSequencing();
+        List<Integer> operationSequencing= new ArrayList<>(chromosome.getOperationSequencing());
         orders.forEach(t->clearFinishOrder(t));
 
 
@@ -546,7 +544,7 @@ public class MaterialRequirementService {
 
             // 调用BuildOperationBOM方法（返回结果对象替代out参数）
             BOMBuildResult operationResult = buildOperationSFBOM(operation.getOrderId(), "",
-                    operation.getQuantity(), operation, 1,demand,sceneId,chromosome);
+                    operation.getQuantity(), operation, 1,demand,sceneId,chromosome,_baseTime,_globalParam);
 //                // 合并物料需求和子订单
             if(operationResult!=null) {
                 allRequirements.addAll(operationResult.getMaterialRequirements());
@@ -567,13 +565,13 @@ public class MaterialRequirementService {
         return allRequirements;
     }
 
-private void clearFinishOrder (Order order)
-{
+    private void clearFinishOrder (Order order)
+    {
         if (order.getFinishOrderId() == null) {
             order.setFinishOrderId(new ArrayList<>());
             order.setTargetFinishedOperationId(new ArrayList<>());
         }
-}
+    }
 
     /**
      * 构建订单BOM
@@ -586,7 +584,7 @@ private void clearFinishOrder (Order order)
      * @return 包含物料需求列表和子订单列表的结果对象
      */
     public BOMBuildResult buildOrderBOM(String sceneId,int parent, String materialID, String mainorderId,
-                                        String childorderId, double parentQuantity, int level,Order forder,int finishOpertionID,Chromosome chromosome) {
+                                        String childorderId, double parentQuantity, int level,Order forder,int finishOpertionID,Chromosome chromosome, LocalDateTime baseTime, GlobalParam globalParam) {
         forder.setCreateBom(true);
 
         List<RoutingHeader>  headers=GetRoutingHeader(sceneId);
@@ -627,7 +625,7 @@ private void clearFinishOrder (Order order)
                 }
                 // 调用BuildOperationBOM方法（返回结果对象替代out参数）
                 BOMBuildResult operationResult = buildOperationBOM(mainorderId, childorderId,
-                        parentQuantity, operation, level,forder,sceneId,chromosome);
+                        parentQuantity, operation, level,forder,sceneId,chromosome, baseTime, globalParam);
 //                // 合并物料需求和子订单
                 if(operationResult!=null) {
                     materialRequirements.addAll(operationResult.getMaterialRequirements());
@@ -650,7 +648,7 @@ private void clearFinishOrder (Order order)
      * @return 包含物料需求列表和子订单列表的结果对象（替代C#的out参数）
      */
     public BOMBuildResult buildOperationBOM(String orderId, String childorderId, double parentQuantity,
-                                            Entry operation, int level,Order forder,String sceneId,Chromosome chromosome) {
+                                            Entry operation, int level,Order forder,String sceneId,Chromosome chromosome, LocalDateTime baseTime, GlobalParam globalParam) {
         List<OrderMaterialRequirement> materialRequirements = new ArrayList<>();
         List<Order> _childorders = new ArrayList<>();
         List<Entry> _newEntrys = new ArrayList<>();
@@ -678,7 +676,7 @@ private void clearFinishOrder (Order order)
                     return null;
                 }
 
-                OrderMaterialRequirement orderMaterial= CreateMaterialRequirement(material,orderId,childorderId,operation,allneeded,component.getSpentQty().doubleValue(), component.getMainQty().doubleValue() , parentQuantity);
+                OrderMaterialRequirement orderMaterial= CreateMaterialRequirement(material,orderId,childorderId,operation,allneeded,component.getSpentQty().doubleValue(), component.getMainQty().doubleValue() , parentQuantity, baseTime);
                 orderMaterial.setLevel(level);
                 orderMaterial.setBomId(component.getStrId());
                 materialRequirements.add(orderMaterial);
@@ -713,7 +711,7 @@ private void clearFinishOrder (Order order)
 
                                 BOMBuildResult childResult = buildOrderBOM(sceneId,0, orderMaterial.getMaterialId(),
                                         orderId, order.getOrderId(),
-                                        order.getQuantity(), l,order,operation.getId(),chromosome);
+                                        order.getQuantity(), l,order,operation.getId(),chromosome, baseTime, globalParam);
                                 //   materialRequirements.addAll(childResult.getMaterialRequirements());
                                 _childorders.addAll(childResult.getChildOrders());
                                 _newEntrys.addAll(childResult.getNewEntrys());
@@ -743,7 +741,7 @@ private void clearFinishOrder (Order order)
                         childorder.setQuantity((int) orderMaterial.getQjQty());
                         childorder.setFinishOrderId(new ArrayList<>());
                         childorder.getFinishOrderId().add(forder.getId());
-                        childorder.setNewCreate(true);
+                        childorder.setNewSfCreate(true);
                         childorder.setTargetFinishedOperationId(new ArrayList<>());
                         childorder.getTargetFinishedOperationId().add(operation.getId());
                         childorder.setSerie(forder.getSerie());
@@ -769,7 +767,7 @@ private void clearFinishOrder (Order order)
                         int l = level + 1;
                         BOMBuildResult childResult = buildOrderBOM(sceneId,0, orderMaterial.getMaterialId(),
                                 orderId, childorder.getOrderId(),
-                                childorder.getQuantity(), l,childorder,0,chromosome);
+                                childorder.getQuantity(), l,childorder,0,chromosome, baseTime, globalParam);
                         //  materialRequirements.addAll(childResult.getMaterialRequirements());
                         _childorders.addAll(childResult.getChildOrders());
                         _newEntrys.addAll(childResult.getNewEntrys());
@@ -792,10 +790,10 @@ private void clearFinishOrder (Order order)
         RoutingHeader headers1=  headers.stream()
                 .filter(t->t.getMaterialId().equals(materialID))
                 .findFirst().orElse(null);
-if(headers1==null)
-{
+        if(headers1==null)
+        {
             return null;
-}
+        }
         routingIds=headers1.getId().longValue();
 
 
@@ -921,7 +919,7 @@ if(headers1==null)
      * @return 包含物料需求列表和子订单列表的结果对象（替代C#的out参数）
      */
     public BOMBuildResult buildOperationSFBOM(String orderId, String childorderId, double parentQuantity,
-                                            Entry operation, int level,Order forder,String sceneId,Chromosome chromosome) {
+                                              Entry operation, int level,Order forder,String sceneId,Chromosome chromosome, LocalDateTime baseTime, GlobalParam globalParam) {
         List<OrderMaterialRequirement> materialRequirements = new ArrayList<>();
         List<Order> _childorders = new ArrayList<>();
         List<Entry> _newEntrys = new ArrayList<>();
@@ -980,7 +978,7 @@ if(headers1==null)
 
                                 BOMBuildResult childResult = buildOrderBOM(sceneId,0, orderMaterial.getMaterialId(),
                                         orderId, order.getOrderId(),
-                                            order.getQuantity(), l,order,operation.getId(),chromosome);
+                                        order.getQuantity(), l,order,operation.getId(),chromosome, baseTime, globalParam);
                                 materialRequirements.addAll(childResult.getMaterialRequirements());
                                 _childorders.addAll(childResult.getChildOrders());
                                 _newEntrys.addAll(childResult.getNewEntrys());
@@ -1010,7 +1008,7 @@ if(headers1==null)
                         childorder.setQuantity((int) orderMaterial.getQjQty());
                         childorder.setFinishOrderId(new ArrayList<>());
                         childorder.getFinishOrderId().add(forder.getId());
-                            childorder.setNewCreate(true);
+                        childorder.setNewSfCreate(true);
                         childorder.setTargetFinishedOperationId(new ArrayList<>());
                         childorder.getTargetFinishedOperationId().add(operation.getId());
                         childorder.setSerie(forder.getSerie());
@@ -1036,7 +1034,7 @@ if(headers1==null)
                         int l = level + 1;
                         BOMBuildResult childResult = buildOrderBOM(sceneId,0, orderMaterial.getMaterialId(),
                                 orderId, childorder.getOrderId(),
-                                    childorder.getQuantity(), l,childorder,0,chromosome);
+                                childorder.getQuantity(), l,childorder,0,chromosome, baseTime, globalParam);
                         materialRequirements.addAll(childResult.getMaterialRequirements());
                         _childorders.addAll(childResult.getChildOrders());
                         _newEntrys.addAll(childResult.getNewEntrys());
@@ -1089,7 +1087,7 @@ if(headers1==null)
     }
 
 
-    private OrderMaterialRequirement CreateMaterialRequirement(Material material,String orderId, String childorderId, Entry operation,double need,double spentQty,double mainQty,double qty) {
+    private OrderMaterialRequirement CreateMaterialRequirement(Material material,String orderId, String childorderId, Entry operation,double need,double spentQty,double mainQty,double qty, LocalDateTime baseTime) {
 
         OrderMaterialRequirement orderMaterial = new OrderMaterialRequirement();
         orderMaterial.setOrderId(orderId);
@@ -1140,7 +1138,7 @@ if(headers1==null)
 
      * @return 包含物料需求列表和子订单列表的结果对象（替代C#的out参数）
      */
-    public void EditOperationBOM(Entry operation,Chromosome chromosome,LocalDateTime earliestStartTime,GeneticDecoder coder,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache) {
+    public void EditOperationBOM(Entry operation,Chromosome chromosome,LocalDateTime earliestStartTime,GeneticDecoder coder,Map<Long, CopyOnWriteArrayList<GAScheduleResult>> machineTasksCache,Map<Integer, Entry> entryIndexById,Map<Integer, GAScheduleResult> scheduleIndexById, LocalDateTime baseTime) {
         List<OrderMaterialRequirement> materialRequirements = new ArrayList<>();
 
         String sceneId=chromosome.getScenarioID();
@@ -1247,7 +1245,7 @@ if(headers1==null)
                     continue;
                 }
 
-                MaterialInTransit(material,"",orderMaterial,needed,earliestStartTime);
+                MaterialInTransit(material,"",orderMaterial,needed,earliestStartTime, materials);
 
                 needed -= orderMaterial.getUseTransit();
 
@@ -1268,7 +1266,7 @@ if(headers1==null)
                             break;
                         }
 
-                        MaterialInTransit(material1,rsr.getMaterialid(),orderMaterial1,needed,earliestStartTime);
+                        MaterialInTransit(material1,rsr.getMaterialid(),orderMaterial1,needed,earliestStartTime, materials);
 
 
                         needed -= orderMaterial1.getUseTransit();
@@ -1319,7 +1317,7 @@ if(headers1==null)
 
                             for (Integer orderid : orderids) {
                                 coder.ClearorderOperationResult(chromosome, orderid);
-                                   coder.EditorderOperation(chromosome, orderid, needed,machineTasksCache);
+                                coder.EditorderOperation(chromosome, orderid, needed,machineTasksCache, entryIndexById, scheduleIndexById);
                             }
 
                         }else {
@@ -1376,14 +1374,14 @@ if(headers1==null)
 
 
 
-    private void MaterialInTransit(Material material,String materialId, OrderMaterialRequirement orderMaterial, double needed,LocalDateTime earliestStartTime ){
+    private void MaterialInTransit(Material material,String materialId, OrderMaterialRequirement orderMaterial, double needed,LocalDateTime earliestStartTime, List<Material> materials ){
 
         // 处理在途物料
         double accumulated = 0;
         LocalDateTime earliestTime = LocalDateTime.of(2000,1,1,0,0,0);
         double useTransit = 0;
         if (material == null) {
-            material = _materials.stream()
+            material = materials.stream()
                     .filter(m -> m.getId().equals(materialId))
                     .findFirst()
                     .orElse(null);

src/main/java/com/aps/service/Algorithm/MaterialRequirementServicebf.java

@@ -894,7 +894,7 @@ public class MaterialRequirementServicebf {
                             childorder.setQuantity((int) orderMaterial.getQjQty());
                             childorder.setFinishOrderId(new ArrayList<>());
                             childorder.getFinishOrderId().add(forder.getId());
-                            childorder.setNewCreate(true);
+                            childorder.setNewSfCreate(true);
                             childorder.setTargetFinishedOperationId(new ArrayList<>());
                             childorder.getTargetFinishedOperationId().add(operation.getId());
                             childorder.setSerie(forder.getSerie());

src/main/java/com/aps/service/Algorithm/ParallelLocalSearch.java

-package com.aps.service.Algorithm;
-
-import com.aps.common.util.ProductionDeepCopyUtil;
-import com.aps.entity.Algorithm.*;
-import com.aps.entity.basic.Entry;
-import com.aps.entity.basic.GlobalParam;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.concurrent.*;
-
-/**
- * 多种局部搜索算法并行
- */
-public class ParallelLocalSearch {
-    private final ExecutorService executor;
-    private GlobalParam globalParam;
-    private List<Entry> allOperations;
-    private List<GlobalOperationInfo> globalOpList;
-    private FitnessCalculator fitnessCalculator;
-    private ObjectiveWeights objectiveWeights;
-
-    public ParallelLocalSearch(GlobalParam globalParam, List<Entry> allOperations, List<GlobalOperationInfo> globalOpList, FitnessCalculator fitnessCalculator, ObjectiveWeights objectiveWeights) {
-        this.globalParam = globalParam;
-        this.allOperations = allOperations;
-        this.globalOpList = globalOpList;
-        this.fitnessCalculator = fitnessCalculator;
-        this.objectiveWeights = objectiveWeights;
-        this.executor = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());
-    }
-
-    /**
-     * 并行局部搜索
-     */
-    public Chromosome search(Chromosome chromosome, GeneticDecoder decoder, ScheduleParams param) {
-        List<Callable<Chromosome>> tasks = new ArrayList<>();
-
-        // 添加爬山法任务
-//        tasks.add(() -> {
-//            HillClimbing hillClimbing = new HillClimbing(globalParam, allOperations, globalOpList, fitnessCalculator, objectiveWeights);
-//            return hillClimbing.search(chromosome, decoder, param);
-//        });
-
-        // 添加变邻域搜索任务
-        tasks.add(() -> {
-            VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch(globalParam, allOperations, globalOpList, fitnessCalculator, objectiveWeights);
-            return vns.search(chromosome, decoder, param);
-        });
-
-        // 添加模拟退火任务
-//        tasks.add(() -> {
-//            SimulatedAnnealing sa = new SimulatedAnnealing(globalParam, allOperations, globalOpList, fitnessCalculator, objectiveWeights);
-//            return sa.search(chromosome, decoder, param);
-//        });
-
-        // 执行所有任务
-        List<Chromosome> results = new ArrayList<>();
-        try {
-            List<Future<Chromosome>> futures = executor.invokeAll(tasks);
-            for (Future<Chromosome> future : futures) {
-                results.add(future.get());
-            }
-        } catch (InterruptedException | ExecutionException e) {
-            e.printStackTrace();
-        }
-
-        // 选择最优解
-        Chromosome best = chromosome;
-        for (Chromosome result : results) {
-            if (isBetter(result, best)) {
-                best = result;
-            }
-        }
-
-        return best;
-    }
-
-    /**
-     * 比较两个染色体的优劣
-     */
-    private boolean isBetter(Chromosome c1, Chromosome c2) {
-        return c1.getFitness() > c2.getFitness();
-    }
-
-    /**
-     * 关闭线程池
-     */
-    public void shutdown() {
-        executor.shutdown();
-    }
-}
\ No newline at end of file

src/main/java/com/aps/service/Algorithm/ScheduleOperationService.java

@@ -1885,7 +1885,7 @@ if(targetOp.getSequence()>1) {
 
 
 
-        decoder.decode(chromosome);
+        decoder.decode(chromosome,false);
 
         KpiCalculator kpiCalculator=new KpiCalculator(chromosome);
         kpiCalculator.calculatekpi();

src/main/java/com/aps/service/Algorithm/SimulatedAnnealing.java

 package com.aps.service.Algorithm;
 
 import com.aps.common.util.FileHelper;
+import com.aps.common.util.GlobalCacheUtil;
 import com.aps.common.util.ProductionDeepCopyUtil;
 import com.aps.entity.Algorithm.*;
-import com.aps.entity.basic.Entry;
-import com.aps.entity.basic.GlobalParam;
-import com.aps.entity.basic.Machine;
-import com.aps.entity.basic.MachineOption;
+import com.aps.entity.Algorithm.IDAndChildID.GroupResult;
+import com.aps.entity.basic.*;
 
 import java.util.*;
 import java.util.concurrent.*;
@@ -18,17 +17,39 @@ import java.util.stream.Collectors;
 public class SimulatedAnnealing {
     private final Random rnd = new Random();
 
+    private void log(String message) {
+        log(message, false);
+    }
+
+    private void log(String message, boolean enableLogging) {
+        if (enableLogging) {
+            FileHelper.writeLogFile(message);
+        }
+    }
+
     private List<Entry> allOperations;
 
-private Map<String, Entry> entrys;
+    private  List<Order> orders;
+    private  List<Material> materials;
+
+    private  List<GroupResult> _entryRel;
+
+    private FitnessCalculator fitnessCalculator;
+
+    private Map<String, Entry> entrys;
     private  Map<Integer, Entry> entrybyids;
 
-    public SimulatedAnnealing( List<Entry> allOperations) {
+    public SimulatedAnnealing( List<Entry> allOperations, List<Order> orders,
+                               List<Material> materials,List<GroupResult> entryRel,  FitnessCalculator _fitnessCalculator) {
 
         this.allOperations = allOperations;
+        this.orders = orders;
+        this.materials = materials;
 
-        Map<Integer, Object> mp = buildEntryKey();
+        _entryRel=entryRel;
 
+        Map<Integer, Object> mp = buildEntryKey();
+        this.fitnessCalculator = _fitnessCalculator;
         entrys=(Map<String, Entry>)mp.get(1);
         entrybyids=(Map<Integer, Entry>)mp.get(2);
 
@@ -40,7 +61,7 @@ private Map<String, Entry> entrys;
             new ArrayBlockingQueue<>(200), // 有界队列，避免内存溢出
             new ThreadPoolExecutor.CallerRunsPolicy() // 任务满了主线程执行，不丢失任务
     );
-    public List<Chromosome> batchSearch(List<Chromosome> chromosomes, GeneticDecoder decoder, List<Machine> machines) {
+    public List<Chromosome> batchSearch(List<Chromosome> chromosomes,VariableNeighborhoodSearch vns, GeneticDecoder decoder, List<Machine> machines) {
         List<Chromosome> saHcOptimized=new ArrayList<>();
 //        CompletableFuture.allOf(chromosomes.stream()
 //                        .map(chromosome -> CompletableFuture.runAsync(() -> {
@@ -50,15 +71,15 @@ private Map<String, Entry> entrys;
 //                        .toArray(CompletableFuture[]::new))
 //                .join();
         for (Chromosome chromosome:chromosomes) {
-            Chromosome optimized = searchWithHillClimbing(chromosome, decoder, machines);
+            Chromosome optimized = searchWithHillClimbing(chromosome,vns, decoder, machines);
             saHcOptimized.add(optimized);
 
         }
         return saHcOptimized;
     }
 
-    public Chromosome batchSearchGetMax(List<Chromosome> chromosomes, GeneticDecoder decoder, List<Machine> machines) {
-        List<Chromosome> saHcOptimized=batchSearch(chromosomes,decoder,machines);
+    public Chromosome batchSearchGetMax(List<Chromosome> chromosomes,VariableNeighborhoodSearch vns, GeneticDecoder decoder, List<Machine> machines) {
+        List<Chromosome> saHcOptimized=batchSearch(chromosomes,vns,decoder,machines);
 
         int bestidx= Getbest(saHcOptimized,null);
         if(bestidx>-1) {
@@ -72,17 +93,18 @@ private Map<String, Entry> entrys;
      * 模拟退火搜索，当温度降低到一定程度后切换到爬山法
      * 流程：模拟退火全局探索（按概率接受劣解）→ 降温 → 温度低时爬山法局部求精 → 输出最优
      */
-    public Chromosome searchWithHillClimbing(Chromosome chromosome, GeneticDecoder decoder, List<Machine> machines) {
-        FileHelper.writeLogFile("模拟退火+爬山法 - 开始执行");
+    public Chromosome searchWithHillClimbing(Chromosome chromosome, VariableNeighborhoodSearch vns,GeneticDecoder decoder, List<Machine> machines) {
+        log("模拟退火+爬山法 - 开始执行",true);
 
         Chromosome current = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
         Chromosome best = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
         writeKpi(best);
-        // 初始化解码
-
 
+        // 记录初始KPI用于计算改进率
+        double[] initialFitnessLevel = best.getFitnessLevel().clone();
+        double initialFitness = best.getFitness();
 
-      //  FileHelper.writeLogFile("模拟退火+爬山法 - 初始化解码完成");
+        //  log("模拟退火+爬山法 - 初始化解码完成");
 
         // 初始化温度
         double temperature = 100.0;
@@ -90,19 +112,26 @@ private Map<String, Entry> entrys;
         double temperatureThreshold = 1.0;
         int maxIterations = 300;
         int noImproveCount = 0;
-        int maxNoImprove = 50;
+        int maxNoImprove = 30; // 降低最大无改进次数
+
+        // 新增：改进率监控参数
+        int stagnantWindow = 15; // 观察窗口大小
+        int[] recentImprovements = new int[stagnantWindow]; // 记录最近窗口内的改进情况
+        double improvementRateThreshold = 0.05; // 改进率阈值(5%)
 
-        FileHelper.writeLogFile(String.format("模拟退火+爬山法 - 参数配置：温度=%.1f, 降温率=%.2f, 阈值=%.1f, 最大迭代=%d",
-                temperature, coolingRate, temperatureThreshold, maxIterations));
+        log(String.format("模拟退火+爬山法 - 参数配置：温度=%.1f, 降温率=%.2f, 阈值=%.1f, 最大迭代=%d, 最大无改进=%d",
+                temperature, coolingRate, temperatureThreshold, maxIterations, maxNoImprove));
 
         int acceptCount = 0;
         int improveCount = 0;
+        int totalIterations = 0;
 
         for (int i = 0; i < maxIterations; i++) {
+            totalIterations = i + 1;
             boolean improved = false;
 
             // 1. 使用智能策略生成邻域解（找瓶颈工序/设备）
-            Chromosome neighbor = generateNeighbor(current);
+            Chromosome neighbor = vns.generateNeighbor(current);
 
             // 2. 解码
             decode(decoder, neighbor,machines);
@@ -124,7 +153,10 @@ private Map<String, Entry> entrys;
                     improved = true;
                     improveCount++;
                     noImproveCount = 0;
-                    FileHelper.writeLogFile(String.format("模拟退火+爬山法 - 迭代%d：找到更优解，fitness=%.4f", i, best.getFitness()));
+
+                    // 记录改进详情
+                    logImprovementDetails(best, initialFitnessLevel, initialFitness, totalIterations);
+                    log(String.format("模拟退火+爬山法 - 迭代%d：找到更优解，fitness=%.4f", totalIterations, best.getFitness()),true);
                 }
             }
 
@@ -132,713 +164,330 @@ private Map<String, Entry> entrys;
                 noImproveCount++;
             }
 
-            // 5. 降温
-            temperature *= coolingRate;
-
-            // 每50次迭代输出一次状态
-            if ((i + 1) % 50 == 0) {
-                FileHelper.writeLogFile(String.format("模拟退火+爬山法 - 迭代%d/%d：温度=%.4f, 接受数=%d, 改进数=%d, 无改进连续=%d",
-                        i + 1, maxIterations, temperature, acceptCount, improveCount, noImproveCount));
+            // 记录本次改进情况
+            if (totalIterations <= stagnantWindow) {
+                recentImprovements[totalIterations - 1] = improved ? 1 : 0;
             }
 
-            // 6. 提前停止条件：温度低于阈值 或 连续多次没改进
-            if (temperature < temperatureThreshold || noImproveCount >= maxNoImprove) {
-                String stopReason = temperature < temperatureThreshold ? "温度低于阈值" : "连续无改进达到上限";
-                FileHelper.writeLogFile(String.format("模拟退火+爬山法 - 提前停止：%s，迭代%d次，最终温度=%.4f",
-                        stopReason, i + 1, temperature));
-
-                FileHelper.writeLogFile("模拟退火+爬山法 - 切换到爬山法求精");
-                HillClimbing hillClimbing = new HillClimbing( allOperations);
-                Chromosome refined = hillClimbing.search(best, decoder, machines);
-                FileHelper.writeLogFile("模拟退火+爬山法 - 爬山法求精完成");
+            // 5. 降温
+            temperature *= coolingRate;
 
-                return refined;
-            }
+            // 每30次迭代输出一次状态
+            if ((totalIterations) % 30 == 0) {
+                log(String.format("模拟退火+爬山法 - 迭代%d/%d：温度=%.4f, 接受数=%d, 改进数=%d, 无改进连续=%d, 总改进率=%.2f%%",
+                        totalIterations, maxIterations, temperature, acceptCount, improveCount, noImproveCount,
+                        totalIterations > 0 ? (double)improveCount / totalIterations * 100 : 0));
             }
 
-        FileHelper.writeLogFile(String.format("模拟退火+爬山法 - 完成所有%d次迭代，最终fitness=%.4f",
-                maxIterations, best.getFitness()));
+            // 6. 提前停止条件
+            boolean shouldStop = false;
+            String stopReason = "";
 
-        // 7. 输出全局最优排产
-        return best;
+            if (temperature < temperatureThreshold) {
+                shouldStop = true;
+                stopReason = "温度低于阈值";
+            } else if (noImproveCount >= maxNoImprove) {
+                shouldStop = true;
+                stopReason = String.format("连续无改进达到上限(%d次)", maxNoImprove);
+            } else if (totalIterations >= stagnantWindow) {
+                // 检查改进率是否过低
+                double recentImproveRate = calculateRecentImprovementRate(recentImprovements, stagnantWindow);
+                if (recentImproveRate < improvementRateThreshold) {
+                    shouldStop = true;
+                    stopReason = String.format("最近%d次迭代改进率过低(%.2f%%)", stagnantWindow, recentImproveRate * 100);
                 }
-    private  void  writeKpi(Chromosome chromosome) {
-        String fitness = "";
-        double[] fitness1 = chromosome.getFitnessLevel();
-        for (int i = 0; i < fitness1.length; i++) {
-
-            fitness += fitness1[i] + ",";
             }
 
-        FileHelper.writeLogFile(String.format("爬山法 - kpi：%s", fitness));
+            if (shouldStop) {
+                log(String.format("模拟退火+爬山法 - 提前停止：%s，迭代%d次，最终温度=%.4f",
+                        stopReason, totalIterations, temperature));
+                logFinalSummary(best, initialFitnessLevel, initialFitness, improveCount, totalIterations);
 
-    }
-
-    /**
-     * 计算能量差（基于fitnessLevel数组的比较）
-     */
-    private double calculateEnergyDifference(Chromosome neighbor, Chromosome current) {
-        double[] neighborFitness = neighbor.getFitnessLevel();
-        double[] currentFitness = current.getFitnessLevel();
+                log("模拟退火+爬山法 - 切换到爬山法求精");
+                HillClimbing hillClimbing = new HillClimbing( allOperations,orders,materials,_entryRel,fitnessCalculator);
+                Chromosome refined = hillClimbing.search(best, decoder, machines);
+                log("模拟退火+爬山法 - 爬山法求精完成");
 
-        // 计算加权能量差
-        double diff = 0;
-        for (int i = 0; i < neighborFitness.length; i++) {
-            diff += (neighborFitness[i] - currentFitness[i]);
+                return refined;
             }
-        return diff;
         }
 
-    /**
-     * 按优先级分组工序
-     */
-    private Map<Double, List<Entry>> groupOperationsByPriority() {
-        Map<Double, List<Entry>> groups = new HashMap<>();
-        for (Entry op : allOperations) {
-            double priority = op.getPriority();
-            groups.computeIfAbsent(priority, k -> new ArrayList<>()).add(op);
-        }
-
-        // 过滤掉：设备只有一个且只有一个GroupId的优先级组
-        Map<Double, List<Entry>> filteredGroups = new HashMap<>();
-        for (Map.Entry<Double, List<Entry>> entry : groups.entrySet()) {
-            List<Entry> ops = entry.getValue();
-
-            // 检查是否所有工序都只有一个设备选项
-            boolean allSingleMachine = ops.stream()
-                    .allMatch(op -> op.getMachineOptions().size() <= 1);
+        log(String.format("模拟退火+爬山法 - 完成所有%d次迭代，最终fitness=%.4f",
+                maxIterations, best.getFitness()),true);
+        logFinalSummary(best, initialFitnessLevel, initialFitness, improveCount, totalIterations);
 
-            // 检查是否只有一个GroupId
-            Set<Integer> groupIds = ops.stream()
-                    .map(Entry::getGroupId)
-                    .collect(Collectors.toSet());
-
-            // 如果两个条件都满足，过滤掉这个优先级组
-            if (!(allSingleMachine && groupIds.size() <= 1)) {
-                filteredGroups.put(entry.getKey(), ops);
-            }
+        // 7. 输出全局最优排产
+        return best;
     }
 
-        return filteredGroups;
-    }
     /**
-     * 构建位置索引：groupId_sequence -> position
+     * 模拟退火搜索
+     * 流程：模拟退火全局探索（按概率接受劣解）→ 降温 → 输出最优
      */
-    private Map<String, Integer> buildPositionIndex(Chromosome chromosome) {
-        Map<String, Integer> index = new HashMap<>();
-        List<Integer> os = chromosome.getOperationSequencing();
-        Map<Integer, Integer> orderProcessCounter = new HashMap<>();
-
-        for (int i = 0; i < os.size(); i++) {
-            int groupId = os.get(i);
-            int count = orderProcessCounter.getOrDefault(groupId, 0) + 1;
-            orderProcessCounter.put(groupId, count);
-
-            String key = groupId + "_" + count;
-            index.put(key, i);
-        }
-        return index;
-    }
+    public Chromosome search(Chromosome chromosome,TabuSearch tabusearch,VariableNeighborhoodSearch vns, GeneticDecoder decoder, List<Machine> machines) {
+        log("模拟退火 - 开始执行",true);
 
-    /**
-     * 构建位置索引：groupId_sequence -> Machines position
-     */
-    private Map<String, Integer> buildEntryMachinePositionIndex(Chromosome chromosome) {
-        Map<String, Integer> index = new HashMap<>();
-        List<GlobalOperationInfo> globalOpList = chromosome.getGlobalOpList();
+        Chromosome current = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
+        decode(decoder, current,machines);
+        Chromosome best = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
+        writeKpi(best);
+        // 初始化解码
 
-        for (int i = 0; i < globalOpList.size(); i++) {
-            GlobalOperationInfo globalOp = globalOpList.get(i);
-            Entry op = globalOp.getOp();
-            int groupId = op.getGroupId();
-            int count = op.getSequence();
+        // 记录初始KPI用于计算改进率
+        double[] initialFitnessLevel = best.getFitnessLevel().clone();
+        double initialFitness = best.getFitness();
 
-            String key = groupId + "_" + count;
-            index.put(key, i);
-        }
-        return index;
-    }
+        //  log("模拟退火+爬山法 - 初始化解码完成");
 
-    /**
-     * 构建位置->Entry索引
-     */
-    private Map<Integer, Entry> buildPositionToEntryIndex(Chromosome chromosome,Map<String, Entry> entrys) {
-        Map<Integer, Entry> index = new HashMap<>();
-        List<Integer> os = chromosome.getOperationSequencing();
-        Map<Integer, Integer> orderProcessCounter = new HashMap<>();
+        // 初始化温度
+        double temperature = 100.0;
+        double coolingRate = 0.95;
+        double temperatureThreshold = 1.0;
+        int maxIterations = 300;
+        int noImproveCount = 0;
+        int maxNoImprove = 20; // 降低最大无改进次数，更快提前结束
 
-        for (int i = 0; i < os.size(); i++) {
-            int groupId = os.get(i);
-            int count = orderProcessCounter.getOrDefault(groupId, 0) + 1;
-            orderProcessCounter.put(groupId, count);
+        // 新增：改进率监控参数
+        int stagnantWindow = 10; // 观察窗口大小
+        int[] recentImprovements = new int[stagnantWindow]; // 记录最近窗口内的改进情况
+        double improvementRateThreshold = 0.001; // 改进率阈值
 
-            String key = groupId + "_" + count;
-            Entry op = entrys.get(key);
+        log(String.format("模拟退火 - 参数配置：温度=%.1f, 降温率=%.2f, 阈值=%.1f, 最大迭代=%d, 最大无改进=%d",
+                temperature, coolingRate, temperatureThreshold, maxIterations, maxNoImprove));
 
-            if (op != null) {
-                index.put(i, op);
-            }
-        }
-        return index;
-    }
-    /**
-     * 构建Entry索引：op.getGroupId() + "_" + op.getSequence() -> Entry
-     */
-    private Map<Integer, Object> buildEntryKey() {
-        Map<Integer, Object> index0 = new HashMap<>();
-        Map<String, Entry> index = new HashMap<>();
-        Map<Integer, Entry> index2 = new HashMap<>();
-        List<Entry> allOps = this.allOperations;
+        int acceptCount = 0;
+        int improveCount = 0;
+        int totalIterations = 0;
 
-        for (Entry op : allOps) {
-            String key = op.getGroupId() + "_" + op.getSequence();
+        for (int i = 0; i < maxIterations; i++) {
+            totalIterations = i + 1;
+            boolean improved = false;
+            log(String.format("模拟退火 - 迭代%d：", totalIterations));
 
-            index.put(key, op);
-            index2.put(op.getId(), op);
-        }
-        index0.put(1,index);
-        index0.put(2,index2);
-        return index0;
+            // 1. 使用智能策略生成邻域解（找瓶颈工序/设备）
+            Chromosome neighbor = vns.generateNeighbor(current);
+            // 2. 解码
+            decode(decoder, neighbor,machines);
+            // 跳过禁忌解（除非是最优解）
+            if (tabusearch.isTabu(neighbor.getGeneStr()) && !isBetter(current,best)) {
+                temperature *= coolingRate;
+                noImproveCount++;
+                // 记录本次无改进
+                if (totalIterations <= stagnantWindow) {
+                    recentImprovements[totalIterations - 1] = 0;
                 }
-
-    /**
-     * 生成邻域解 - 智能策略：优先处理瓶颈工序/设备
-     * 策略：
-     * 1. 优先处理瓶颈设备上的工序（换设备）
-     * 2. 瓶颈设备上的工序往前移动
-     * 3. 非瓶颈设备上的工序往后移动（给瓶颈工序腾位置）
-     */
-    private Chromosome generateNeighbor(Chromosome chromosome) {
-        List<GAScheduleResult> results = chromosome.getResult();
-
-        if (results == null || results.isEmpty()) {
-            return generateRandomNeighbor(chromosome, entrys);
+                continue;
             }
 
-        // 构建位置索引
-        Map<String, Integer> positionIndex = buildPositionIndex(chromosome);
-        Map<Integer, Entry> positionToEntryIndex = buildPositionToEntryIndex(chromosome, entrys);
-        Map<String, Integer> MachinePositionIndex = buildEntryMachinePositionIndex(chromosome);
-
-        // 1. 找出瓶颈设备
-        Long bottleneckMachineId = findBottleneckMachine(chromosome);
+            // 3. 计算能量差
+            double energyDifference = calculateEnergyDifference(neighbor, current);
 
-        if (bottleneckMachineId != null) {
-            // 找出瓶颈设备上的工序
-            List<GAScheduleResult> bottleneckOps = findOpsOnBottleneckMachine(chromosome, bottleneckMachineId);
+            // 4. 按概率接受新解（模拟退火核心：有概率接受劣解）
+            boolean accepted = false;
+            if (energyDifference > 0 || rnd.nextDouble() < Math.exp(energyDifference / temperature)) {
+                current = neighbor;
 
-            if (!bottleneckOps.isEmpty()) {
-                // 70%概率优先处理瓶颈设备
-                if (rnd.nextDouble() < 0.7) {
-                    int strategy = rnd.nextInt(10);
+                acceptCount++;
+                if (isBetter(current, best)) {
+                    best = ProductionDeepCopyUtil.deepCopy(current, Chromosome.class);
+                    tabusearch.addToTabuList(best.getGeneStr());
+                    writeKpi(best);
+                    improved = true;
+                    improveCount++;
+                    noImproveCount = 0;
 
-                       if(strategy<=3)
-                        {
-                            // 策略1：瓶颈设备上的工序换设备
-                            return tryChangeMachineForBottleneckOp(chromosome, bottleneckOps, entrys, MachinePositionIndex);
-                        }
-                    else if(strategy<=8) {
-                           // 策略2：瓶颈设备上的工序往前移动
-                           return tryMoveBottleneckOpForward(chromosome, bottleneckOps, positionIndex, positionToEntryIndex, entrys);
-                       }else if(strategy<=10) {
-                           // 策略3：非瓶颈设备上的工序往后移动
-                           return tryMoveNonBottleneckOpBackward(chromosome, bottleneckMachineId, positionIndex, positionToEntryIndex, entrys);
-                       }
-                }
+                    // 记录改进详情
+                    logImprovementDetails(best, initialFitnessLevel, initialFitness, totalIterations);
+                    log(String.format("模拟退火 - 迭代%d：找到更优解，fitness=%.4f", totalIterations, best.getFitness()));
                 }
             }
 
-        // 默认策略：随机邻域
-        return generateRandomNeighbor(chromosome, entrys);
-    }
-    /**
-     * 识别瓶颈设备（利用率最高的设备）
-     */
-    private Long findBottleneckMachine(Chromosome chromosome) {
-        Map<Long, Double> utilization = calculateMachineUtilization(chromosome);
-        if (utilization.isEmpty()) {
-            return null;
+            if (!improved) {
+                noImproveCount++;
             }
 
-        return utilization.entrySet().stream()
-                .max(Map.Entry.comparingByValue())
-                .map(Map.Entry::getKey)
-                .orElse(null);
+            // 记录本次改进情况
+            if (totalIterations <= stagnantWindow) {
+                recentImprovements[totalIterations - 1] = improved ? 1 : 0;
             }
 
-    /**
-     * 查找在瓶颈设备上的工序
-     */
-    private List<GAScheduleResult> findOpsOnBottleneckMachine(Chromosome chromosome, Long bottleneckMachineId) {
-        if (bottleneckMachineId == null) {
-            return new ArrayList<>();
-        }
+            // 5. 降温
+            temperature *= coolingRate;
 
-        return chromosome.getResult().stream()
-                .filter(r -> r.getMachineId() == bottleneckMachineId)
-                .collect(Collectors.toList());
+            // 每次迭代都输出状态
+            log(String.format("模拟退火 - 迭代%d/%d：温度=%.4f, 接受数=%d, 改进数=%d, 无改进连续=%d, 总改进率=%.2f%%",
+                    totalIterations, maxIterations, temperature, acceptCount, improveCount, noImproveCount,
+                    totalIterations > 0 ? (double)improveCount / totalIterations * 100 : 0));
+
+            // 6. 检查提前停止条件
+            boolean shouldStop = false;
+            String stopReason = "";
+
+            if (temperature < temperatureThreshold) {
+                shouldStop = true;
+                stopReason = "温度低于阈值";
+            } else if (noImproveCount >= maxNoImprove) {
+                shouldStop = true;
+                stopReason = String.format("连续无改进达到上限(%d次)", maxNoImprove);
+            } else if (totalIterations >= stagnantWindow) {
+                // 检查改进率是否过低
+                double recentImproveRate = calculateRecentImprovementRate(recentImprovements, stagnantWindow);
+                if (recentImproveRate < improvementRateThreshold) {
+                    shouldStop = true;
+                    stopReason = String.format("最近%d次迭代改进率过低(%.2f%%)", stagnantWindow, recentImproveRate * 100);
+                }
+            }
+
+            if (shouldStop) {
+                log(String.format("模拟退火 - 提前停止：%s，迭代%d次，最终温度=%.4f",
+                        stopReason, totalIterations, temperature));
+                logFinalSummary(best, initialFitnessLevel, initialFitness, improveCount, totalIterations);
+                return best;
             }
-    /**
-     * 计算设备利用率
-     */
-    private Map<Long, Double> calculateMachineUtilization(Chromosome chromosome) {
-        Map<Long, Double> utilization = new HashMap<>();
-        List<GAScheduleResult> results = chromosome.getResult();
-
-        if (results == null || results.isEmpty()) {
-            return utilization;
         }
 
-        int maxEndTime = results.stream()
-                .mapToInt(GAScheduleResult::getEndTime)
-                .max()
-                .orElse(1);
+        log(String.format("模拟退火 - 完成所有%d次迭代，最终fitness=%.4f",
+                maxIterations, best.getFitness()),true);
+        logFinalSummary(best, initialFitnessLevel, initialFitness, improveCount, totalIterations);
 
-        Map<Long, List<GAScheduleResult>> machineResults = results.stream()
-                .collect(Collectors.groupingBy(GAScheduleResult::getMachineId));
-
-        for (Map.Entry<Long, List<GAScheduleResult>> entry : machineResults.entrySet()) {
-            long machineId = entry.getKey();
-            List<GAScheduleResult> machineOps = entry.getValue();
-
-            int totalProcessingTime = machineOps.stream()
-                    .mapToInt(r -> r.getEndTime() - r.getStartTime())
-                    .sum();
-
-            double util = (double) totalProcessingTime / maxEndTime;
-            utilization.put(machineId, util);
+        // 7. 输出全局最优排产
+        return best;
     }
 
-        return utilization;
-    }
     /**
-     * 尝试给瓶颈设备上的工序换设备
+     * 记录改进详情
      */
-    private Chromosome tryChangeMachineForBottleneckOp(Chromosome chromosome, List<GAScheduleResult> bottleneckOps, Map<String, Entry> entrys,Map<String, Integer> MachinePositionIndex) {
-        // 优先选择有多设备选项的工序
-        List<GAScheduleResult> candidates = bottleneckOps.stream()
-                .filter(op -> {
-                    Entry entry = entrybyids.get(op.getOperationId());
-                    return entry != null && entry.getMachineOptions().size() > 1;
-                })
-                .collect(Collectors.toList());
-
-        if (candidates.isEmpty()) {
-            return generateRandomNeighbor(chromosome, entrys);
-        }
-
-        // 从候选中随机选择一个工序，给它换设备
-        GAScheduleResult selectedOp = candidates.get(rnd.nextInt(candidates.size()));
+    private void logImprovementDetails(Chromosome best, double[] initialFitnessLevel, double initialFitness, int iteration) {
+        StringBuilder sb = new StringBuilder("模拟退火 - 改进详情: 迭代" + iteration + ", ");
+        double[] currentFitness = best.getFitnessLevel();
 
-        // 找到这个工序在globalOpList中的位置
-        int maPos = -1;
-
-       Entry entry= entrybyids.get(selectedOp.getOperationId());
-        if (entry != null && entry.getMachineOptions().size() > 1) {
-            maPos = MachinePositionIndex.get(entry.getGroupId() + "_" + entry.getSequence());
-            if (maPos >= 0) {
-                return generateMachineChangeForSpecificOp(chromosome, entry.getMachineOptions(), maPos);
-            }
-        }
-        // 直接换设备
-        return generateMachineChangeNeighbor(chromosome);
-    }
-    /**
-     * 给指定位置的工序换设备
-     */
-    private Chromosome generateMachineChangeForSpecificOp(Chromosome chromosome, List<MachineOption> machineOptions, int idx) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> ms = neighbor.getMachineSelection();
-        if (!ms.isEmpty() && idx >= 0 && idx < ms.size()) {
-            if (machineOptions.size() > 1) {
-                int currentMachineSeq = ms.get(idx);
-                List<Integer> availableMachines = new ArrayList<>();
-                for (int i = 1; i <= machineOptions.size(); i++) {
-                    if (i != currentMachineSeq) {
-                        availableMachines.add(i);
-                    }
+        for (int i = 0; i < currentFitness.length; i++) {
+            double improvement = currentFitness[i] - initialFitnessLevel[i];
+            sb.append(String.format("KPI%d: %.4f→%.4f(+%.4f) ", i+1, initialFitnessLevel[i], currentFitness[i], improvement));
         }
 
-                if (!availableMachines.isEmpty()) {
-                    int newMachineSeq = availableMachines.get(rnd.nextInt(availableMachines.size()));
-                    ms.set(idx, newMachineSeq);
-                    neighbor.setMachineSelection(ms);
-                }
-            }
-        }
-        return neighbor;
+        double totalImprovement = best.getFitness() - initialFitness;
+        sb.append(String.format("总Fitness: %.4f→%.4f(+%.4f)", initialFitness, best.getFitness(), totalImprovement));
+        log(sb.toString());
     }
 
     /**
-     * 尝试将瓶颈设备上的工序往前移动
+     * 计算最近改进率
      */
-    private Chromosome tryMoveBottleneckOpForward(Chromosome chromosome, List<GAScheduleResult> bottleneckOps,
-                                                  Map<String, Integer> positionIndex,
-                                                  Map<Integer, Entry> positionToEntryIndex,
-                                                  Map<String, Entry> entrys) {
-        if (bottleneckOps.isEmpty()) {
-            return generateSwapNeighbor(chromosome, entrys);
-        }
-
-        // 随机选择一个瓶颈工序
-       // GAScheduleResult randomOp = bottleneckOps.get(rnd.nextInt(bottleneckOps.size()));
-        // 计算所有订单的完成时间
-        List<GAScheduleResult> allResults = chromosome.getResult();
-        Map<Integer, Integer> orderCompletionTimes = calculateOrderCompletionTimes(allResults);
-
-        // 找出最晚完成的订单（延期候选）
-        int latestCompletionTime = orderCompletionTimes.values().stream()
-                .mapToInt(Integer::intValue)
-                .max()
-                .orElse(0);
-
-        // 按优先级和订单完成时间排序：优先选择高优先级+延期订单的工序
-        List<GAScheduleResult> sortedBottleneckOps = new ArrayList<>(bottleneckOps);
-        sortedBottleneckOps.sort((a, b) -> {
-            Entry entryA = entrybyids.get(a.getOperationId());
-            Entry entryB = entrybyids.get(b.getOperationId());
-
-            if (entryA != null && entryB != null) {
-                // 首先比较优先级（高优先级在前）
-                int priorityCompare = Double.compare(entryB.getPriority(), entryA.getPriority());
-                if (priorityCompare != 0) {
-                    return priorityCompare;
-                }
-
-                // 优先级相同，比较订单完成时间（晚完成的在前，即更可能延期）
-                int endTimeA = orderCompletionTimes.getOrDefault(a.getGroupId(), 0);
-                int endTimeB = orderCompletionTimes.getOrDefault(b.getGroupId(), 0);
-                return Integer.compare(endTimeB, endTimeA);
-            }
-            return 0;
-        });
-        GAScheduleResult selectedOp = sortedBottleneckOps.get(rnd.nextInt(Math.min(5, sortedBottleneckOps.size())));
-
-        Entry entry=entrybyids.get(selectedOp.getOperationId()) ;
-        String key = entry.getGroupId() + "_" + entry.getSequence();
-        Integer opPos = positionIndex.get(key);
-
-        if (opPos == null || opPos <= 0) {
-            return generateSwapNeighbor(chromosome, entrys);
-        }
-
-        // 往前移动1-3步
-       // int steps = rnd.nextInt(3) + 1;
-       // return moveOperationForward(chromosome,positionToEntryIndex, opPos, steps);
-        // 使用插入方式往前移动（1-5步）
-        int steps = rnd.nextInt(5) + 1;
-        return insertOperationForward(chromosome, opPos, steps, entrys);
-    }
-    /**
-     * 计算每个订单的最后完成时间
-     */
-    private Map<Integer, Integer> calculateOrderCompletionTimes(List<GAScheduleResult> allResults) {
-        Map<Integer, Integer> orderEndTimes = new HashMap<>();
-        for (GAScheduleResult result : allResults) {
-            int groupId = result.getGroupId();
-            int endTime = result.getEndTime();
-            orderEndTimes.put(groupId, Math.max(orderEndTimes.getOrDefault(groupId, 0), endTime));
-        }
-        return orderEndTimes;
-    }
-    /**
-     * 尝试将非瓶颈设备上的工序往后移动（给瓶颈工序腾位置）
-     */
-    private Chromosome tryMoveNonBottleneckOpBackward(Chromosome chromosome, Long bottleneckMachineId,
-                                                      Map<String, Integer> positionIndex,
-                                                      Map<Integer, Entry> positionToEntryIndex,
-                                                      Map<String, Entry> entrys) {
-        List<GAScheduleResult> results = chromosome.getResult();
-        if (results == null) {
-            return generateSwapNeighbor(chromosome, entrys);
-        }
-
-        // 找出非瓶颈设备上的工序
-        List<GAScheduleResult> nonBottleneckOps = results.stream()
-                .filter(r -> r.getMachineId() != bottleneckMachineId)
-                .collect(Collectors.toList());
-
-        if (nonBottleneckOps.isEmpty()) {
-            return generateSwapNeighbor(chromosome, entrys);
+    private double calculateRecentImprovementRate(int[] recentImprovements, int windowSize) {
+        int improveCount = 0;
+        for (int i = 0; i < windowSize; i++) {
+            improveCount += recentImprovements[i];
         }
-
-        // 随机选择一个非瓶颈工序
-        GAScheduleResult randomOp = nonBottleneckOps.get(rnd.nextInt(nonBottleneckOps.size()));
-       Entry entry=entrybyids.get(randomOp.getOperationId());
-        String key = entry.getGroupId()+"_"+entry.getSequence();
-        Integer opPos = positionIndex.get(key);
-
-        if (opPos == null || opPos >= chromosome.getOperationSequencing().size() - 1) {
-            return generateSwapNeighbor(chromosome, entrys);
+        return (double) improveCount / windowSize;
     }
 
-        // 往后移动1-3步
-        int steps = rnd.nextInt(3) + 1;
-        return moveOperationBackward(chromosome,positionToEntryIndex, opPos, steps);
-    }
     /**
-     * 将工序往前移动
+     * 记录最终总结
      */
-    private Chromosome moveOperationForward(Chromosome chromosome, Map<Integer, Entry> originalPositionIndex,int pos, int steps) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
+    private void logFinalSummary(Chromosome best, double[] initialFitnessLevel, double initialFitness, int improveCount, int totalIterations) {
+        StringBuilder sb = new StringBuilder("模拟退火 - 最终总结: ");
+        double[] currentFitness = best.getFitnessLevel();
 
-        if (pos <= 0 || pos >= os.size()) {
-            return neighbor;
-        }
+        sb.append(String.format("总迭代%d次, 成功改进%d次, 改进率%.2f%%. ",
+                totalIterations, improveCount, totalIterations > 0 ? (double)improveCount / totalIterations * 100 : 0));
 
-        // 获取要移动的工序和目标位置的工序（基于原始chromosome）
-        Entry opToMove = originalPositionIndex.get(pos);
-        if (opToMove == null) {
-            return neighbor;
+        for (int i = 0; i < currentFitness.length; i++) {
+            double improvement = currentFitness[i] - initialFitnessLevel[i];
+            sb.append(String.format("KPI%d: %.4f→%.4f(%.2f%%) ", i+1, initialFitnessLevel[i], currentFitness[i],
+                    initialFitnessLevel[i] > 0 ? improvement / initialFitnessLevel[i] * 100 : 0));
         }
 
-        int targetPos = Math.max(0, pos - steps);
-        Entry targetOp = originalPositionIndex.get(targetPos);
-
-        // 检查是否可以交换
-        if (targetOp != null && opToMove.getGroupId() == targetOp.getGroupId()) {
-            return neighbor;
-        }
-        if (targetOp != null && Math.abs(targetOp.getPriority() - opToMove.getPriority()) > 0.001) {
-            return neighbor;
+        double totalImprovement = best.getFitness() - initialFitness;
+        sb.append(String.format("总Fitness: %.4f→%.4f(%.2f%%)", initialFitness, best.getFitness(),
+                initialFitness > 0 ? totalImprovement / initialFitness * 100 : 0));
+        log(sb.toString());
     }
 
-        // 直接交换到目标位置
-        java.util.Collections.swap(os, pos, targetPos);
-        neighbor.setOperationSequencing(os);
-        return neighbor;
-    }
 
-    /**
-     * 将工序往后移动
-     */
-    private Chromosome moveOperationBackward(Chromosome chromosome,Map<Integer, Entry> originalPositionIndex, int pos, int steps) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-
-        if (pos < 0 || pos >= os.size() - 1) {
-            return neighbor;
-        }
-
-        // 获取要移动的工序和目标位置的工序（基于原始chromosome）
+    private  void  writeKpi(Chromosome chromosome) {
+        String fitness = "";
+        double[] fitness1 = chromosome.getFitnessLevel();
+        for (int i = 0; i < fitness1.length; i++) {
 
-        Entry opToMove = originalPositionIndex.get(pos);
-        if (opToMove == null) {
-            return neighbor;
+            fitness += fitness1[i] + ",";
         }
 
-        int targetPos = Math.min(os.size() - 1, pos + steps);
-        Entry targetOp = originalPositionIndex.get(targetPos);
+        log(String.format("模拟退火 - kpi：%s", fitness),true);
 
-        // 检查是否可以交换
-        if (targetOp != null && opToMove.getGroupId() == targetOp.getGroupId()) {
-            return neighbor;
-        }
-        if (targetOp != null && Math.abs(targetOp.getPriority() - opToMove.getPriority()) > 0.001) {
-            return neighbor;
-        }
-
-        // 直接交换到目标位置
-        java.util.Collections.swap(os, pos, targetPos);
-        neighbor.setOperationSequencing(os);
-        return neighbor;
     }
 
     /**
-     * 将工序往前插入（不交换，直接插入到目标位置）
+     * 计算能量差（基于fitnessLevel数组的比较）
      */
-    private Chromosome insertOperationForward(Chromosome chromosome, int pos, int steps, Map<String, Entry> entrys) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-
-        if (pos <= 0 || pos >= os.size()) {
-            return neighbor;
-        }
-
-        // 获取要移动的工序（基于原始chromosome）
-        Map<Integer, Entry> originalPositionIndex = buildPositionToEntryIndex(chromosome, entrys);
-        Entry opToMove = originalPositionIndex.get(pos);
-        if (opToMove == null) {
-            return neighbor;
-        }
-
-        // 找到最远可以插入的位置（不打破优先级）
-        int targetPos = Math.max(0, pos - steps);
-        int finalTargetPos = pos;
+    private double calculateEnergyDifference(Chromosome neighbor, Chromosome current) {
+        double[] neighborFitness = neighbor.getFitnessLevel();
+        double[] currentFitness = current.getFitnessLevel();
 
-        for (int i = pos - 1; i >= targetPos; i--) {
-            Entry leftOp = originalPositionIndex.get(i);
-            if (leftOp != null && opToMove.getGroupId() == leftOp.getGroupId()) {
-                break;
-            }
-            if (leftOp != null && Math.abs(leftOp.getPriority() - opToMove.getPriority()) > 0.001) {
-                break;
-            }
-            finalTargetPos = i;
+        // 计算加权能量差
+        double diff = 0;
+        for (int i = 0; i < neighborFitness.length; i++) {
+            diff += (neighborFitness[i] - currentFitness[i]);
         }
-
-        if (finalTargetPos == pos) {
-            return neighbor;
+        return diff;
     }
 
-        // 执行插入：移除原位置，插入到目标位置
-        int value = os.remove(pos);
-        os.add(finalTargetPos, value);
-        neighbor.setOperationSequencing(os);
-        return neighbor;
-    }
-    /**
-     * 随机生成邻域解（兜底策略）
-     */
-    private Chromosome generateRandomNeighbor(Chromosome chromosome, Map<String, Entry> entrys) {
-        int neighborType = rnd.nextInt(4);
-        switch (neighborType) {
-            case 0:
-                return generateSwapNeighbor(chromosome, entrys);
-            case 1:
-                return generateReverseNeighbor(chromosome, entrys);
-            case 2:
-                return generateInsertNeighbor(chromosome, entrys);
-            case 3:
-                return generateMachineChangeNeighbor(chromosome);
-            default:
-                return generateSwapNeighbor(chromosome, entrys);
-        }
-    }
     /**
-     * 生成交换邻域解 - 只在相同优先级的工序之间交换
+     * 按优先级分组工序
      */
-    private Chromosome generateSwapNeighbor(Chromosome chromosome,Map<String, Entry> entrys) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            Map<Integer, Entry> positionIndex = buildPositionToEntryIndex(neighbor,entrys);
-
-            int maxAttempts = 50;
-            for (int attempt = 0; attempt < maxAttempts; attempt++) {
-                int idx1 = rnd.nextInt(os.size());
-                int idx2 = rnd.nextInt(os.size());
-
-                if (idx1 == idx2) continue;
-
-                Entry op1 = positionIndex.get(idx1);
-                Entry op2 = positionIndex.get(idx2);
-
-                if (op1 != null && op2 != null) {
-                    if (Math.abs(op1.getPriority() - op2.getPriority()) < 0.001) {
-                        java.util.Collections.swap(os, idx1, idx2);
-                        neighbor.setOperationSequencing(os);
-                        break;
-                    }
-                }
-            }
-        }
-        return neighbor;
+    private Map<Double, List<Entry>> groupOperationsByPriority() {
+        Map<Double, List<Entry>> groups = new HashMap<>();
+        for (Entry op : allOperations) {
+            double priority = op.getPriority();
+            groups.computeIfAbsent(priority, k -> new ArrayList<>()).add(op);
         }
 
-    /**
-     * 生成反转邻域解 - 只反转相同优先级的连续工序
-     */
-    private Chromosome generateReverseNeighbor(Chromosome chromosome,Map<String, Entry> entrys) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            Map<Integer, Entry> positionIndex = buildPositionToEntryIndex(neighbor,entrys);
-
-            int maxAttempts = 50;
-            for (int attempt = 0; attempt < maxAttempts; attempt++) {
-                int start = rnd.nextInt(os.size());
-                int end = rnd.nextInt(os.size());
-
-                if (end <= start) continue;
+        // 过滤掉：设备只有一个且只有一个GroupId的优先级组
+        Map<Double, List<Entry>> filteredGroups = new HashMap<>();
+        for (Map.Entry<Double, List<Entry>> entry : groups.entrySet()) {
+            List<Entry> ops = entry.getValue();
 
-                Entry startOp = positionIndex.get(start);
-                if (startOp == null) continue;
+            // 检查是否所有工序都只有一个设备选项
+            boolean allSingleMachine = ops.stream()
+                    .allMatch(op -> op.getMachineOptions().size() <= 1);
 
-                double priority = startOp.getPriority();
-                boolean allSamePriority = true;
+            // 检查是否只有一个GroupId
+            Set<Integer> groupIds = ops.stream()
+                    .map(Entry::getGroupId)
+                    .collect(Collectors.toSet());
 
-                for (int i = start; i <= end; i++) {
-                    Entry op = positionIndex.get(i);
-                    if (op == null || Math.abs(op.getPriority() - priority) > 0.001) {
-                        allSamePriority = false;
-                        break;
+            // 如果两个条件都满足，过滤掉这个优先级组
+            if (!(allSingleMachine && groupIds.size() <= 1)) {
+                filteredGroups.put(entry.getKey(), ops);
             }
         }
 
-                if (allSamePriority) {
-                    java.util.Collections.reverse(os.subList(start, end + 1));
-                    neighbor.setOperationSequencing(os);
-                    break;
-                }
-            }
-        }
-        return neighbor;
+        return filteredGroups;
     }
 
     /**
-     * 生成插入邻域解 - 只插入到相同优先级的位置
+     * 构建Entry索引：op.getGroupId() + "_" + op.getSequence() -> Entry
      */
-    private Chromosome generateInsertNeighbor(Chromosome chromosome,Map<String, Entry> entrys) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            Map<Integer, Entry> positionIndex = buildPositionToEntryIndex(neighbor,entrys);
-
-            int maxAttempts = 50;
-            for (int attempt = 0; attempt < maxAttempts; attempt++) {
-                int idx1 = rnd.nextInt(os.size());
-                int idx2 = rnd.nextInt(os.size());
-
-                if (idx1 == idx2) continue;
+    private Map<Integer, Object> buildEntryKey() {
+        Map<Integer, Object> index0 = new HashMap<>();
+        Map<String, Entry> index = new HashMap<>();
+        Map<Integer, Entry> index2 = new HashMap<>();
+        List<Entry> allOps = this.allOperations;
 
-                Entry op1 = positionIndex.get(idx1);
-                Entry op2 = positionIndex.get(idx2);
+        for (Entry op : allOps) {
+            String key = op.getGroupId() + "_" + op.getSequence();
 
-                if (op1 != null && op2 != null) {
-                    if (Math.abs(op1.getPriority() - op2.getPriority()) < 0.001) {
-                        int value = os.remove(idx1);
-                        os.add(idx2, value);
-                        neighbor.setOperationSequencing(os);
-                        break;
-                    }
-                }
-            }
+            index.put(key, op);
+            index2.put(op.getId(), op);
         }
-        return neighbor;
+        index0.put(1,index);
+        index0.put(2,index2);
+        return index0;
     }
 
     /**
-     * 生成机器选择邻域解 - 不受优先级限制
+     * 解码染色体
      */
-    private Chromosome generateMachineChangeNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> ms = neighbor.getMachineSelection();
-        if (!ms.isEmpty()) {
-            int idx = rnd.nextInt(ms.size());
-            GlobalOperationInfo globalOp = chromosome.getGlobalOpList().get(idx);
-            Entry op = globalOp.getOp();
-
-            if (op.getMachineOptions().size() > 1) {
-                int currentMachineSeq = ms.get(idx);
-                List<Integer> availableMachines = new ArrayList<>();
-                for (int i = 1; i <= op.getMachineOptions().size(); i++) {
-                    if (i != currentMachineSeq) {
-                        availableMachines.add(i);
-                    }
-                }
-
-                if (!availableMachines.isEmpty()) {
-                    int newMachineSeq = availableMachines.get(rnd.nextInt(availableMachines.size()));
-                    ms.set(idx, newMachineSeq);
-                    neighbor.setMachineSelection(ms);
-                }
-            }
-        }
-        return neighbor;
-    }
-
     /**
      * 解码染色体
      */
@@ -847,24 +496,35 @@ private Map<String, Entry> entrys;
 
         // 假设Machine类有拷贝方法，或使用MapStruct等工具进行映射
 
-        chromosome.setMachines(ProductionDeepCopyUtil.deepCopyList(machines, Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setOrders(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(orders), Order.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setOperatRel(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(_entryRel), GroupResult.class) ); // 简单拷贝，实际可能需要深拷贝
+        chromosome.setMaterials(ProductionDeepCopyUtil.deepCopyList(materials,Material.class)); // 简单拷贝，实际可能需要深拷贝
 
-        decoder.decodeChromosomeWithCache(chromosome);
+        chromosome.setAllOperations(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(allOperations), Entry.class) ); // 简单拷贝，实际可能需要深拷贝
+        //chromosome.setObjectiveWeights(_objectiveWeights);
+        //  chromosome.setBaseTime(param.getBaseTime());
+        //  chromosome.setInitMachines(ProductionDeepCopyUtil.deepCopyList(machines,Machine.class)); // 简单拷贝，实际可能需要深拷贝
+        //   _sceneService.saveChromosomeToFile(chromosome, "12345679");
 
+        // 加载锁定工单到ResultOld
+        List<GAScheduleResult> lockedOrders = GlobalCacheUtil.get("locked_orders_" + chromosome.getScenarioID());
+        if (lockedOrders != null && !lockedOrders.isEmpty()) {
+            chromosome.setResultOld(ProductionDeepCopyUtil.deepCopyList(lockedOrders, GAScheduleResult.class));
+            log("将 " + lockedOrders.size() + " 个锁定工单加载到初始种群中");
+        } else {
+            chromosome.setResultOld(new CopyOnWriteArrayList<>());
         }
+        decoder.decodeChromosomeWithCache(chromosome,true);
+
+    }
+
 
     /**
      * 比较两个染色体的优劣（基于fitnessLevel多层次比较）
      */
     private boolean isBetter(Chromosome c1, Chromosome c2) {
-        for (int i = 0; i < c1.getFitnessLevel().length; i++) {
-            double[] Fitness1 = c1.getFitnessLevel();
-            double[] Fitness2 = c2.getFitnessLevel();
-            if (Fitness1[i] > Fitness2[i]) {
-                return true;
-            }
-        }
-        return false;
+        return fitnessCalculator.isBetter(c1,c2);
     }
     private int  Getbest(List<Chromosome> candidates,Chromosome best) {
         // 找出最佳候选方案

src/main/java/com/aps/service/Algorithm/TabuSearch.java

 package com.aps.service.Algorithm;
 
+import com.aps.common.util.FileHelper;
+import com.aps.common.util.GlobalCacheUtil;
 import com.aps.common.util.ProductionDeepCopyUtil;
 import com.aps.entity.Algorithm.*;
-import com.aps.entity.basic.Entry;
-import com.aps.entity.basic.GlobalParam;
+import com.aps.entity.Algorithm.IDAndChildID.GroupResult;
+import com.aps.entity.basic.*;
 
 import java.util.*;
+import java.util.concurrent.CopyOnWriteArrayList;
 
 /**
  * 禁忌搜索算法
  */
 public class TabuSearch {
-    private final Random rnd = new Random();
-    private GlobalParam globalParam;
-    private List<Entry> allOperations;
-    private List<GlobalOperationInfo> globalOpList;
-    private FitnessCalculator fitnessCalculator;
-    private ObjectiveWeights objectiveWeights;
 
+
+    private void log(String message) {
+        log(message, false);
+    }
+
+    private void log(String message, boolean enableLogging) {
+        if (enableLogging) {
+            FileHelper.writeLogFile(message);
+        }
+    }
+
+    private FitnessCalculator fitnessCalculator;
     // 禁忌表
-    private List<Chromosome> tabuList;
+    private List<String> tabuList;
     private int tabuListSize = 50;
 
-    public TabuSearch(GlobalParam globalParam, List<Entry> allOperations, List<GlobalOperationInfo> globalOpList, FitnessCalculator fitnessCalculator, ObjectiveWeights objectiveWeights) {
-        this.globalParam = globalParam;
-        this.allOperations = allOperations;
-        this.globalOpList = globalOpList;
-        this.fitnessCalculator = fitnessCalculator;
-        this.objectiveWeights = objectiveWeights;
+    private List<Machine> cachedMachines;
+    private List<Order> cachedOrders;
+    private List<GroupResult> cachedEntryRel;
+    private List<Material> cachedMaterials;
+    private List<Entry> cachedAllOperations;
+    // 渴望准则：记录最优解的 fitness
+    private double[] bestFitness;
+
+    public TabuSearch(List<Entry> allOperations, List<Order> orders,
+                      List<Material> materials,List<GroupResult> entryRel,  FitnessCalculator _fitnessCalculator) {
+
         this.tabuList = new ArrayList<>();
         // 工序越多，禁忌表越长（适配1000+工序）
         this.tabuListSize = Math.min(50, allOperations.size() / 30);
+        fitnessCalculator=_fitnessCalculator;
+
+        // 预缓存解码需要的深拷贝列表，避免重复拷贝
+        cachedAllOperations = ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(allOperations), Entry.class);
+        cachedOrders = ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(orders), Order.class);
+        cachedEntryRel = ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(entryRel), GroupResult.class);
+        cachedMaterials = ProductionDeepCopyUtil.deepCopyList(materials, Material.class);
+
     }
 
     /**
-     * 禁忌搜索
+     * 禁忌搜索（优化版）
      */
-    public Chromosome search(Chromosome chromosome, GeneticDecoder decoder, ScheduleParams param) {
+    public Chromosome search(Chromosome chromosome, VariableNeighborhoodSearch vns,
+                             GeneticDecoder decoder, List<Machine> machines) {
+        log("禁忌搜索 - 开始执行",true);
+
         Chromosome current = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
         Chromosome best = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
+        this.bestFitness = best.getFitnessLevel().clone();
+writeKpi(best);
+        // 记录初始KPI用于计算改进率
+        double[] initialFitnessLevel = best.getFitnessLevel().clone();
+        double initialFitness = best.getFitness();
 
         int iterations = 0;
-        int maxIterations = 1000;
+        int improveCount = 0;
         int noImprovementCount = 0;
-        int maxNoImprovement = 100;
+        int maxNoImprovement = 15;  // 降低到15次无改进则停止
+        int maxIterations = Math.min(cachedAllOperations.size(), 80);  // 降低到最多80次
+
+        // 改进率监控
+        int stagnantWindow = 10;
+        int[] recentImprovements = new int[stagnantWindow];
+        double improvementRateThreshold = 0.1; // 10%改进率阈值
 
-        while (iterations < maxIterations && noImprovementCount < maxNoImprovement) {
-            // 生成邻域解
-            List<Chromosome> neighbors = generateNeighbors(current);
+        // 多样性参数
+        int diversificationInterval = 20; // 每20次迭代尝试一次多样化
+        int lastDiversificationIteration = 0;
 
-            // 过滤禁忌解
-            List<Chromosome> validNeighbors = filterTabuSolutions(neighbors);
+        log(String.format("禁忌搜索 - 参数：最大迭代=%d, 最大无改进=%d", maxIterations, maxNoImprovement));
 
-            // 评估所有邻域解
-            Chromosome bestNeighbor = null;
-            double bestNeighborFitness = Double.NEGATIVE_INFINITY;
+        for (int i = 0; i < maxIterations; i++) {
+            iterations++;
+
+            // 使用 VNS 生成邻域解（成功率排序后的策略）
+            Chromosome neighbor = vns.generateNeighbor(current);
 
-            for (Chromosome neighbor : validNeighbors) {
-                decode(decoder, neighbor, param);
-                if (neighbor.getFitness() > bestNeighborFitness) {
-                    bestNeighborFitness = neighbor.getFitness();
-                    bestNeighbor = neighbor;
+            if (neighbor == null) {
+                log("禁忌搜索 - 生成邻居失败，跳过");
+                noImprovementCount++;
+                // 记录无改进
+                if (iterations <= stagnantWindow) {
+                    recentImprovements[iterations - 1] = 0;
+                }
+                continue;
+            }
+
+            // 先检查禁忌（不需要解码！只需要 machineSelection 和 operationSequencing）
+            String neighborGeneStr = neighbor.getGeneStr();
+            String currentGeneStr = current.getGeneStr();
+            boolean isTabu = isTabu(neighborGeneStr);
+
+            // 方案 1：快速跳过和 current 一样的解（不用解码！）
+            if (neighborGeneStr.equals(currentGeneStr)) {
+                // 和当前解一样，跳过，不用解码
+                addToTabuList(neighborGeneStr);
+                noImprovementCount++;
+                if (iterations <= stagnantWindow) {
+                    recentImprovements[iterations - 1] = 0;
                 }
+                continue;
             }
 
-            if (bestNeighbor != null) {
-                // 更新当前解
-                current = bestNeighbor;
+            // 添加到禁忌表（无论是否接受都先记下来）
+            addToTabuList(neighborGeneStr);
 
-                // 更新禁忌表
-                addToTabuList(current);
+            boolean accept = false;
+            boolean isBetterThanBest = false;
+            boolean isBetterThanCurrent = false;
+
+            // 解码（需要比较 fitness）
+            decode(decoder, neighbor, machines);
+
+            isBetterThanBest = isBetter(neighbor, best);
+            isBetterThanCurrent = isBetter(neighbor, current);
+
+            // 判断是否接受这个邻居
+            if (!isTabu) {
+                // 非禁忌，接受
+                accept = true;
+            } else if (isBetterThanBest) {
+                // 禁忌但比最优解好（渴望准则），接受
+                log("禁忌搜索 - 触发渴望准则，接受禁忌解");
+                accept = true;
+            }
+            // else：禁忌且没有比最优解好，不接受
+
+            boolean improvedThisIteration = false;
+
+            if (accept) {
+                // 接受这个邻居
+                current = ProductionDeepCopyUtil.deepCopy(neighbor, Chromosome.class);
 
                 // 更新最优解
-                if (isBetter(current, best)) {
-                    best = current;
+                if (isBetterThanBest) {
+                    best = ProductionDeepCopyUtil.deepCopy(current, Chromosome.class);
+                    this.bestFitness = best.getFitnessLevel().clone();
+                    writeKpi(best);
                     noImprovementCount = 0;
+                    improveCount++;
+                    improvedThisIteration = true;
+                    logTabuImprovement(best, initialFitnessLevel, initialFitness, iterations);
+                    log(String.format("禁忌搜索 - 找到更好解，迭代=%d", iterations),true);
+                } else if (isBetterThanCurrent) {
+                    // 比当前解好但没有比最优解好
+                    noImprovementCount = 0;
+                    improvedThisIteration = true;
                 } else {
+                    // 没有改进
                     noImprovementCount++;
                 }
+            } else {
+                // 不接受，无改进计数+1
+                noImprovementCount++;
             }
 
-            iterations++;
+            // 记录本次改进情况
+            if (iterations <= stagnantWindow) {
+                recentImprovements[iterations - 1] = improvedThisIteration ? 1 : 0;
             }
 
-        return best;
+            // 每10次迭代输出一次状态
+            if (iterations % 10 == 0) {
+                log(String.format("禁忌搜索 - 迭代%d/%d, 改进数=%d, 无改进连续=%d, 改进率=%.2f%%",
+                        iterations, maxIterations, improveCount, noImprovementCount,
+                        iterations > 0 ? (double)improveCount / iterations * 100 : 0));
             }
 
-    /**
-     * 生成邻域解
-     */
-    private List<Chromosome> generateNeighbors(Chromosome chromosome) {
-        List<Chromosome> neighbors = new ArrayList<>();
-
-        // 生成多个邻域解
-        for (int i = 0; i < 10; i++) {
-            int neighborType = rnd.nextInt(4);
-            switch (neighborType) {
-                case 0:
-                    neighbors.add(generateSwapNeighbor(chromosome));
-                    break;
-                case 1:
-                    neighbors.add(generateReverseNeighbor(chromosome));
-                    break;
-                case 2:
-                    neighbors.add(generateInsertNeighbor(chromosome));
-                    break;
-                case 3:
-                    neighbors.add(generateMachineChangeNeighbor(chromosome));
-                    break;
-            }
-        }
+            // 检查提前停止条件
+            boolean shouldStop = false;
+            String stopReason = "";
 
-        return neighbors;
+            if (noImprovementCount >= maxNoImprovement) {
+                shouldStop = true;
+                stopReason = String.format("连续%d次无改进", maxNoImprovement);
+            } else if (iterations >= stagnantWindow) {
+                double recentImproveRate = calculateRecentImprovementRate(recentImprovements, stagnantWindow);
+                if (recentImproveRate < improvementRateThreshold) {
+                    shouldStop = true;
+                    stopReason = String.format("最近%d次迭代改进率过低(%.2f%%)", stagnantWindow, recentImproveRate * 100);
+                }
             }
 
-    /**
-     * 过滤禁忌解
-     */
-    private List<Chromosome> filterTabuSolutions(List<Chromosome> neighbors) {
-        List<Chromosome> validNeighbors = new ArrayList<>();
-
-        for (Chromosome neighbor : neighbors) {
-            if (!isTabu(neighbor)) {
-                validNeighbors.add(neighbor);
+            if (shouldStop) {
+                log(String.format("禁忌搜索 - 提前停止：%s", stopReason));
+                logTabuFinalSummary(best, initialFitnessLevel, initialFitness, iterations, improveCount);
+                break;
             }
         }
 
-        // 如果没有有效邻域解，返回所有邻域解
-        if (validNeighbors.isEmpty()) {
-            return neighbors;
+        logTabuFinalSummary(best, initialFitnessLevel, initialFitness, iterations, improveCount);
+        log(String.format("禁忌搜索 - 结束，总迭代=%d", iterations),true);
+        return best;
     }
+    private  void  writeKpi(Chromosome chromosome) {
+        String fitness = "";
+        double[] fitness1 = chromosome.getFitnessLevel();
+        for (int i = 0; i < fitness1.length; i++) {
 
-        return validNeighbors;
+            fitness += fitness1[i] + ",";
         }
 
+        log(String.format("变邻域搜索 - kpi：%s", fitness),true);
+
+    }
     /**
-     * 检查解是否在禁忌表中
+     * 记录禁忌搜索改进详情
      */
-    private boolean isTabu(Chromosome chromosome) {
-        for (Chromosome tabuChromosome : tabuList) {
-            if (chromosome.getGeneStr().equals(tabuChromosome.getGeneStr())) {
-                return true;
-            }
+    private void logTabuImprovement(Chromosome best, double[] initialFitnessLevel, double initialFitness, int iteration) {
+        StringBuilder sb = new StringBuilder("禁忌搜索 - 改进详情: 迭代" + iteration + ", ");
+        double[] currentFitness = best.getFitnessLevel();
+
+        for (int i = 0; i < currentFitness.length; i++) {
+            double improvement = currentFitness[i] - initialFitnessLevel[i];
+            sb.append(String.format("KPI%d: %.4f→%.4f(+%.4f) ", i+1, initialFitnessLevel[i], currentFitness[i], improvement));
         }
-        return false;
+
+        double totalImprovement = best.getFitness() - initialFitness;
+        sb.append(String.format("总Fitness: %.4f→%.4f(+%.4f)", initialFitness, best.getFitness(), totalImprovement));
+        log(sb.toString());
     }
 
     /**
-     * 添加解到禁忌表
+     * 计算最近改进率
      */
-    private void addToTabuList(Chromosome chromosome) {
-        tabuList.add(chromosome);
-        if (tabuList.size() > tabuListSize) {
-            tabuList.remove(0);
+    private double calculateRecentImprovementRate(int[] recentImprovements, int windowSize) {
+        int improveCount = 0;
+        for (int i = 0; i < windowSize; i++) {
+            improveCount += recentImprovements[i];
         }
+        return (double) improveCount / windowSize;
     }
 
     /**
-     * 生成交换邻域解
+     * 记录禁忌搜索最终总结
      */
-    private Chromosome generateSwapNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int idx1 = rnd.nextInt(os.size());
-            int idx2 = rnd.nextInt(os.size());
-            while (idx2 == idx1) {
-                idx2 = rnd.nextInt(os.size());
-            }
-            Collections.swap(os, idx1, idx2);
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
+    private void logTabuFinalSummary(Chromosome best, double[] initialFitnessLevel, double initialFitness,
+                                     int totalIterations, int improveCount) {
+        StringBuilder sb = new StringBuilder("禁忌搜索 - 最终总结: ");
+        double[] currentFitness = best.getFitnessLevel();
+
+        sb.append(String.format("总迭代%d次, 成功改进%d次, 改进率%.2f%%. ",
+                totalIterations, improveCount, totalIterations > 0 ? (double)improveCount / totalIterations * 100 : 0));
+
+        for (int i = 0; i < currentFitness.length; i++) {
+            double improvement = currentFitness[i] - initialFitnessLevel[i];
+            sb.append(String.format("KPI%d: %.4f→%.4f(%.2f%%) ", i+1, initialFitnessLevel[i], currentFitness[i],
+                    initialFitnessLevel[i] > 0 ? improvement / initialFitnessLevel[i] * 100 : 0));
         }
 
+        double totalImprovement = best.getFitness() - initialFitness;
+        sb.append(String.format("总Fitness: %.4f→%.4f(%.2f%%)", initialFitness, best.getFitness(),
+                initialFitness > 0 ? totalImprovement / initialFitness * 100 : 0));
+        log(sb.toString());
+    }
     /**
-     * 生成反转邻域解
+     * 检查解是否在禁忌表中
      */
-    private Chromosome generateReverseNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int start = rnd.nextInt(os.size());
-            int end = rnd.nextInt(os.size());
-            while (end <= start) {
-                end = rnd.nextInt(os.size());
-            }
-            Collections.reverse(os.subList(start, end + 1));
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
+    public boolean isTabu(String GeneStr) {
+
+        return tabuList.contains(GeneStr);
+
     }
 
     /**
-     * 生成插入邻域解
+     * 添加解到禁忌表（FIFO策略）
      */
-    private Chromosome generateInsertNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int idx1 = rnd.nextInt(os.size());
-            int idx2 = rnd.nextInt(os.size());
-            while (idx2 == idx1) {
-                idx2 = rnd.nextInt(os.size());
-            }
-            int value = os.remove(idx1);
-            os.add(idx2, value);
-            neighbor.setOperationSequencing(os);
+    public void addToTabuList(String geneStr) {
+        tabuList.add(geneStr);
+        if (tabuList.size() > tabuListSize) {
+            // 移除最早加入的禁忌解
+            tabuList.remove(0);
         }
-        return neighbor;
     }
 
-    /**
-     * 生成机器选择邻域解
-     */
-    private Chromosome generateMachineChangeNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> ms = neighbor.getMachineSelection();
-        if (!ms.isEmpty()) {
-            int idx = rnd.nextInt(ms.size());
-            GlobalOperationInfo globalOp = globalOpList.get(idx);
-            Entry op = globalOp.getOp();
 
-            if (op.getMachineOptions().size() > 1) {
-                int currentMachineSeq = ms.get(idx);
-                List<Integer> availableMachines = new ArrayList<>();
-                for (int i = 1; i <= op.getMachineOptions().size(); i++) {
-                    if (i != currentMachineSeq) {
-                        availableMachines.add(i);
-                    }
-                }
+    private void decode(GeneticDecoder decoder, Chromosome chromosome , List<Machine> machines) {
+        chromosome.setResult(new CopyOnWriteArrayList<>());
 
-                if (!availableMachines.isEmpty()) {
-                    int newMachineSeq = availableMachines.get(rnd.nextInt(availableMachines.size()));
-                    ms.set(idx, newMachineSeq);
-                    neighbor.setMachineSelection(ms);
-                }
-            }
-        }
-        return neighbor;
+        // 缓存 Machine 列表（第一次调用时缓存）
+        if (cachedMachines == null) {
+            cachedMachines = ProductionDeepCopyUtil.deepCopyList(machines, Machine.class);
         }
 
-    /**
-     * 解码染色体
-     */
-    private void decode(GeneticDecoder decoder, Chromosome chromosome, ScheduleParams param) {
-        chromosome.setResult(new java.util.concurrent.CopyOnWriteArrayList<>());
-        decoder.decodeChromosomeWithCache(chromosome);
-        if (chromosome.getFitness() == 0) {
-            chromosome.setFitness(fitnessCalculator.calculateFitness(chromosome, objectiveWeights));
+        // 使用缓存的列表，避免重复深拷贝
+        chromosome.setMachines(ProductionDeepCopyUtil.deepCopyList(cachedMachines, Machine.class));
+        chromosome.setOrders(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(cachedOrders), Order.class));
+        chromosome.setOperatRel(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(cachedEntryRel), GroupResult.class));
+        chromosome.setMaterials(ProductionDeepCopyUtil.deepCopyList(cachedMaterials, Material.class));
+        chromosome.setAllOperations(ProductionDeepCopyUtil.deepCopyList(new CopyOnWriteArrayList<>(cachedAllOperations), Entry.class));
+
+        // 加载锁定工单到ResultOld
+        List<GAScheduleResult> lockedOrders = GlobalCacheUtil.get("locked_orders_" + chromosome.getScenarioID());
+        if (lockedOrders != null && !lockedOrders.isEmpty()) {
+            chromosome.setResultOld(ProductionDeepCopyUtil.deepCopyList(lockedOrders, GAScheduleResult.class));
+        } else {
+            chromosome.setResultOld(new CopyOnWriteArrayList<>());
         }
+        decoder.decodeChromosomeWithCache(chromosome,true);
     }
 
+
     /**
-     * 比较两个染色体的优劣
+     * 比较两个染色体的优劣（基于fitnessLevel多层次比较）
      */
     private boolean isBetter(Chromosome c1, Chromosome c2) {
-        return c1.getFitness() > c2.getFitness();
+        return fitnessCalculator.isBetter(c1,c2);
     }
+
+
+
+
+
+
+
+
 }
\ No newline at end of file

src/main/java/com/aps/service/Algorithm/TabuSearchWithSA.java

-package com.aps.service.Algorithm;
-
-import com.aps.common.util.ProductionDeepCopyUtil;
-import com.aps.entity.Algorithm.*;
-import com.aps.entity.basic.Entry;
-import com.aps.entity.basic.GlobalParam;
-
-import java.util.*;
-
-/**
- * 禁忌搜索+模拟退火混合算法
- */
-public class TabuSearchWithSA {
-    private final Random rnd = new Random();
-    private GlobalParam globalParam;
-    private List<Entry> allOperations;
-    private List<GlobalOperationInfo> globalOpList;
-    private FitnessCalculator fitnessCalculator;
-    private ObjectiveWeights objectiveWeights;
-
-    // 禁忌表
-    private List<Chromosome> tabuList;
-    private int tabuListSize = 50;
-
-    public TabuSearchWithSA(GlobalParam globalParam, List<Entry> allOperations, List<GlobalOperationInfo> globalOpList, FitnessCalculator fitnessCalculator, ObjectiveWeights objectiveWeights) {
-        this.globalParam = globalParam;
-        this.allOperations = allOperations;
-        this.globalOpList = globalOpList;
-        this.fitnessCalculator = fitnessCalculator;
-        this.objectiveWeights = objectiveWeights;
-        this.tabuList = new ArrayList<>();
-    }
-
-    /**
-     * 禁忌搜索+模拟退火混合搜索
-     */
-    public Chromosome search(Chromosome chromosome, GeneticDecoder decoder, ScheduleParams param) {
-        Chromosome current = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        Chromosome best = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-
-        // 初始化温度
-        double temperature = 100.0;
-        double coolingRate = 0.95;
-        int iterations = 0;
-        int maxIterations = 1000;
-        int noImprovementCount = 0;
-        int maxNoImprovement = 100;
-
-        while (iterations < maxIterations && noImprovementCount < maxNoImprovement) {
-            // 生成邻域解
-            List<Chromosome> neighbors = generateNeighbors(current);
-
-            // 过滤禁忌解
-            List<Chromosome> validNeighbors = filterTabuSolutions(neighbors);
-
-            // 评估所有邻域解
-            Chromosome bestNeighbor = null;
-            double bestNeighborFitness = Double.NEGATIVE_INFINITY;
-
-            for (Chromosome neighbor : validNeighbors) {
-                decode(decoder, neighbor, param);
-                if (neighbor.getFitness() > bestNeighborFitness) {
-                    bestNeighborFitness = neighbor.getFitness();
-                    bestNeighbor = neighbor;
-                }
-            }
-
-            if (bestNeighbor != null) {
-                // 计算能量差
-                decode(decoder, current, param);
-                double energyDifference = bestNeighbor.getFitness() - current.getFitness();
-
-                // 结合模拟退火的概率接受机制
-                if (energyDifference > 0 || rnd.nextDouble() < Math.exp(energyDifference / temperature)) {
-                    // 更新当前解
-                    current = bestNeighbor;
-
-                    // 更新禁忌表
-                    addToTabuList(current);
-
-                    // 更新最优解
-                    if (isBetter(current, best)) {
-                        best = current;
-                        noImprovementCount = 0;
-                    } else {
-                        noImprovementCount++;
-                    }
-                }
-            }
-
-            // 降温
-            temperature *= coolingRate;
-            iterations++;
-        }
-
-        return best;
-    }
-
-    /**
-     * 生成邻域解
-     */
-    private List<Chromosome> generateNeighbors(Chromosome chromosome) {
-        List<Chromosome> neighbors = new ArrayList<>();
-
-        // 生成多个邻域解
-        for (int i = 0; i < 10; i++) {
-            int neighborType = rnd.nextInt(4);
-            switch (neighborType) {
-                case 0:
-                    neighbors.add(generateSwapNeighbor(chromosome));
-                    break;
-                case 1:
-                    neighbors.add(generateReverseNeighbor(chromosome));
-                    break;
-                case 2:
-                    neighbors.add(generateInsertNeighbor(chromosome));
-                    break;
-                case 3:
-                    neighbors.add(generateMachineChangeNeighbor(chromosome));
-                    break;
-            }
-        }
-
-        return neighbors;
-    }
-
-    /**
-     * 过滤禁忌解
-     */
-    private List<Chromosome> filterTabuSolutions(List<Chromosome> neighbors) {
-        List<Chromosome> validNeighbors = new ArrayList<>();
-
-        for (Chromosome neighbor : neighbors) {
-            if (!isTabu(neighbor)) {
-                validNeighbors.add(neighbor);
-            }
-        }
-
-        // 如果没有有效邻域解，返回所有邻域解
-        if (validNeighbors.isEmpty()) {
-            return neighbors;
-        }
-
-        return validNeighbors;
-    }
-
-    /**
-     * 检查解是否在禁忌表中
-     */
-    private boolean isTabu(Chromosome chromosome) {
-        for (Chromosome tabuChromosome : tabuList) {
-            if (chromosome.getGeneStr().equals(tabuChromosome.getGeneStr())) {
-                return true;
-            }
-        }
-        return false;
-    }
-
-    /**
-     * 添加解到禁忌表
-     */
-    private void addToTabuList(Chromosome chromosome) {
-        tabuList.add(chromosome);
-        if (tabuList.size() > tabuListSize) {
-            tabuList.remove(0);
-        }
-    }
-
-    /**
-     * 生成交换邻域解
-     */
-    private Chromosome generateSwapNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int idx1 = rnd.nextInt(os.size());
-            int idx2 = rnd.nextInt(os.size());
-            while (idx2 == idx1) {
-                idx2 = rnd.nextInt(os.size());
-            }
-            Collections.swap(os, idx1, idx2);
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
-    }
-
-    /**
-     * 生成反转邻域解
-     */
-    private Chromosome generateReverseNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int start = rnd.nextInt(os.size());
-            int end = rnd.nextInt(os.size());
-            while (end <= start) {
-                end = rnd.nextInt(os.size());
-            }
-            Collections.reverse(os.subList(start, end + 1));
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
-    }
-
-    /**
-     * 生成插入邻域解
-     */
-    private Chromosome generateInsertNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> os = neighbor.getOperationSequencing();
-        if (os.size() >= 2) {
-            int idx1 = rnd.nextInt(os.size());
-            int idx2 = rnd.nextInt(os.size());
-            while (idx2 == idx1) {
-                idx2 = rnd.nextInt(os.size());
-            }
-            int value = os.remove(idx1);
-            os.add(idx2, value);
-            neighbor.setOperationSequencing(os);
-        }
-        return neighbor;
-    }
-
-    /**
-     * 生成机器选择邻域解
-     */
-    private Chromosome generateMachineChangeNeighbor(Chromosome chromosome) {
-        Chromosome neighbor = ProductionDeepCopyUtil.deepCopy(chromosome, Chromosome.class);
-        List<Integer> ms = neighbor.getMachineSelection();
-        if (!ms.isEmpty()) {
-            int idx = rnd.nextInt(ms.size());
-            GlobalOperationInfo globalOp = globalOpList.get(idx);
-            Entry op = globalOp.getOp();
-
-            if (op.getMachineOptions().size() > 1) {
-                int currentMachineSeq = ms.get(idx);
-                List<Integer> availableMachines = new ArrayList<>();
-                for (int i = 1; i <= op.getMachineOptions().size(); i++) {
-                    if (i != currentMachineSeq) {
-                        availableMachines.add(i);
-                    }
-                }
-
-                if (!availableMachines.isEmpty()) {
-                    int newMachineSeq = availableMachines.get(rnd.nextInt(availableMachines.size()));
-                    ms.set(idx, newMachineSeq);
-                    neighbor.setMachineSelection(ms);
-                }
-            }
-        }
-        return neighbor;
-    }
-
-    /**
-     * 解码染色体
-     */
-    private void decode(GeneticDecoder decoder, Chromosome chromosome, ScheduleParams param) {
-        chromosome.setResult(new java.util.concurrent.CopyOnWriteArrayList<>());
-        decoder.decodeChromosomeWithCache(chromosome);
-        if (chromosome.getFitness() == 0) {
-            chromosome.setFitness(fitnessCalculator.calculateFitness(chromosome, objectiveWeights));
-        }
-    }
-
-    /**
-     * 比较两个染色体的优劣
-     */
-    private boolean isBetter(Chromosome c1, Chromosome c2) {
-        return c1.getFitness() > c2.getFitness();
-    }
-}
\ No newline at end of file

src/main/java/com/aps/service/plan/PlanResultService.java

@@ -222,7 +222,7 @@ public class PlanResultService {
             lockedOrderProcessorService.markLockedOrdersOccupiedTime(machines, timeConfig.getBaseTime());
 
             // 5. 执行调度算法
-            GeneticAlgorithm scheduler =new GeneticAlgorithm(globalParam,machines,orders,Materials1,materialIds,machineScheduler,entryRel,materialRequirementService,_sceneService,SceneId); //new GeneticAlgorithm(products, machines, orders, machineScheduler);
+            HybridAlgorithm scheduler =new HybridAlgorithm(globalParam,machines,orders,Materials1,materialIds,machineScheduler,entryRel,materialRequirementService,_sceneService,SceneId); //new GeneticAlgorithm(products, machines, orders, machineScheduler);
             param.initAdaptiveParams(entrys.size());
             double[] customWeights = new double[] { 0.4, 0.1, 0.1, 0.1, 0.3 }; // 延迟时间权重提升到0.5
             //完工时间、总流程时间、总换型时间、机器负载标准差、延迟时间
@@ -238,7 +238,7 @@ public class PlanResultService {
 
             _sceneService.saveChromosomeToFile(chromosome, SceneId);
 
-            WriteScheduleSummary(chromosome);
+          //  WriteScheduleSummary(chromosome);
 
             return chromosome;
 

src/main/java/com/aps/service/plan/SceneService.java

@@ -132,7 +132,7 @@ public class SceneService {
         try {
             ObjectMapper objectMapper = createObjectMapper();
 
-            SceneChromsome sceneChromsome=(SceneChromsome)redisUtils.get("SceneId."+sceneId);
+            SceneChromsome sceneChromsome=null;//(SceneChromsome)redisUtils.get("SceneId."+sceneId);
             if(sceneChromsome==null)
             {
                 sceneChromsome=new SceneChromsome();
@@ -163,7 +163,7 @@ public class SceneService {
             sceneChromsome.getSceneDetails().add(sceneDetail);
 
 
-            redisUtils.set("SceneId."+sceneId,sceneChromsome);
+         //   redisUtils.set("SceneId."+sceneId,sceneChromsome);
 
             File file = getChromosomeFile(sceneId,sceneChromsome.getVersion().toString());
 

src/test/java/com/aps/demo/PlanResultServiceTest.java

@@ -39,7 +39,8 @@ public class PlanResultServiceTest {
       //  TestSortService  sortService=new TestSortService();
      //  sortService.test1();
        // nsgaiiUtils.Test();
-      planResultService.execute2("0428340BB4F540938F1FB5599F03E8A4");//2000
+        planResultService.execute2("08B1D87FE1B84ECDBAC2E546DDB6FB81");//2000
+     // planResultService.execute2("0428340BB4F540938F1FB5599F03E8A4");//2000
        // planResultService.execute2("C8B533BD8944405B9A2F8823C575C204");//500
        // planResultService.execute2("EFDD34E4B5BC434BAEAE6A84DFCD4E7B");//20
       //  planResultService.execute2("00E0C5D3E4AD4F36B56C39395906618D");