增加注释，变异实现逻辑修改，增加了企业层变异后的初始化
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@ -140,8 +140,9 @@ class ChromosomeUtils:
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max_total_cap = self.supplier.Cj_total_max[supplier_id] # 供应商总产能上限
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if total_cap > max_total_cap: # 超出上限时缩放
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scale = max_total_cap / total_cap # 缩放比例
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scale = max_total_cap / total_cap # 缩放比例（会出现浮点）
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start = 0
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# 重新遍历，按比例缩小该企业所有物料的产能
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for i in range(self.I):
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end = split_points[i]
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ents = self.material_optional_enterprises[i]
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@ -160,8 +161,8 @@ class ChromosomeUtils:
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repaired = quantity_layer.copy()
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split_points = np.cumsum(self.material_enterprise_count)
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start = 0
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for i in range(self.I):
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qi = self.order.Q[i]
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for i in range(self.I): # 遍历每种物料
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qi = self.order.Q[i] # 物料i的需求量
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end = split_points[i]
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ents = self.material_optional_enterprises[i]
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segment = repaired[start:end]
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@ -184,7 +185,7 @@ class ChromosomeUtils:
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segment[idx] = min_q
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elif segment[idx] > max_q:
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segment[idx] = max_q
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# 步骤2：强制总量等于需求数量（核心修复）
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# 步骤2：强制总量等于需求数量
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current_total = np.sum(segment[selected_indices])
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if current_total <= 0:
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weights = np.random.rand(len(selected_indices))
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@ -91,7 +91,7 @@ class Config:
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def __init__(self):
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# 种群参数
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self.pop_size = 50 # 种群大小
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self.pop_size = 300 # 种群大小
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self.N1_ratio = 0.2 # 优先成本的种群比例
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self.N2_ratio = 0.2 # 优先延期的种群比例
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self.N3_ratio = 0.3 # 强制风险企业的种群比例
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@ -99,12 +99,12 @@ class Config:
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# 遗传操作参数
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self.crossover_prob = 0.8 # 交叉概率
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self.mutation_prob = 0.3 # 变异概率
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self.max_generations = 100 # 最大进化代数
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self.mutation_prob = 0.2 # 变异概率
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self.max_generations = 800 # 最大进化代数
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# 惩罚系数
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self.delta = 1.3 # 变更惩罚系数
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self.gamma = 0.8 # 提前交付惩罚系数
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self.gamma = 500 # 提前交付惩罚系数
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# 早停参数
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self.early_stop_patience = 50 # 连续多少代无改进则早停
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12
encoder.py
12
encoder.py
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@ -1,9 +1,9 @@
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import random
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import numpy as np
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from data_structures import Config # 配置参数类
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from chromosome_utils import ChromosomeUtils # 染色体工具类
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from objective_calculator import ObjectiveCalculator # 目标函数计算器
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from nsga2 import NSGA2 # NSGA-II算法类
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from data_structures import Config
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from chromosome_utils import ChromosomeUtils
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from objective_calculator import ObjectiveCalculator
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from nsga2 import NSGA2
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class Encoder:
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@ -109,8 +109,8 @@ class Encoder:
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if count <= 0: # 数量为0时返回空数组
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return np.array([])
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# 生成候选解（数量为count的2倍或至少20个，确保有足够选择空间）
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candidate_count = max(2 * count, 20)
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# 生成候选解（数量为count的3倍或至少20个，确保有足够选择空间）
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candidate_count = max(3 * count, 20)
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candidates = [self._generate_random_chromosome() for _ in range(candidate_count)]
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# 计算候选解的目标函数值
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@ -52,67 +52,113 @@ class GeneticOperator:
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# 拆分染色体为三层
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enterprise_layer, capacity_layer, quantity_layer = self.utils._split_chromosome(mutated)
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# 计算各物料的企业编码在染色体中的分割点（用于分层处理）
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split_points = np.cumsum(self.utils.material_enterprise_count)
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# 1. 企业层变异（0/1翻转，即是否选择该企业）
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start = 0 # 起始索引
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for i in range(self.utils.I): # 遍历每种物料
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end = split_points[i] # 当前物料的企业编码结束索引
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# 遍历该物料的所有可选企业
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for idx in range(start, end):
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# 以设定的变异概率进行翻转
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if random.random() < self.config.mutation_prob:
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enterprise_layer[idx] = 1 - enterprise_layer[idx] # 0→1或1→0
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start = end # 更新起始索引为下一个物料
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# 修复企业层（确保每种物料至少选择一个企业）
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enterprise_layer = self.utils.repair_enterprise_layer(enterprise_layer)
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# ========== 第一步：记录原始企业层状态 ==========
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original_enterprise_layer = enterprise_layer.copy()
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# 2. 能力层变异（调整选中企业的产能）
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# ========== 第二步：企业层变异 ==========
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start = 0
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for i in range(self.utils.I):
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end = split_points[i]
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ents = self.utils.material_optional_enterprises[i] # 当前物料的可选企业列表
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e_segment = enterprise_layer[start:end] # 当前物料的企业选择状态
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for idx in range(start, end):
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# 仅对被选中的企业（e_segment[idx-start]==1）进行变异
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if random.random() < self.config.mutation_prob and e_segment[idx - start] == 1:
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ent = ents[idx - start] # 企业ID（0为风险企业，1+为供应商）
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# 确定该企业的最大产能
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if ent == 0:
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max_cap = self.utils.risk.C0_i_std[i] # 风险企业的单物料产能
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else:
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supplier_id = ent - 1 # 供应商索引（转换为0基）
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max_cap = self.utils.supplier.Cj_i_std[supplier_id][i] # 供应商的单物料产能
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# 随机生成1到max_cap之间的新产能
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capacity_layer[idx] = random.uniform(1, max_cap)
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if random.random() < self.config.mutation_prob:
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enterprise_layer[idx] = 1 - enterprise_layer[idx]
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start = end
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# 修复能力层（确保不超过企业总产能约束）
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capacity_layer = self.utils.repair_capacity_layer(enterprise_layer, capacity_layer)
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# 3. 数量层变异（调整选中企业的生产数量）
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# 修复企业层
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enterprise_layer = self.utils.repair_enterprise_layer(enterprise_layer)
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# ========== 第三步：同步更新能力和数量层 ==========
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start = 0
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for i in range(self.utils.I):
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end = split_points[i]
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ents = self.utils.material_optional_enterprises[i]
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for idx in range(start, end):
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current_ent_selected = enterprise_layer[idx] # 变异后的选择状态
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original_ent_selected = original_enterprise_layer[idx] # 变异前的选择状态
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# 情况1：企业从选中变为未选中（1→0）
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if original_ent_selected == 1 and current_ent_selected == 0:
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capacity_layer[idx] = 0 # 产能设为0
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quantity_layer[idx] = 0 # 数量设为0
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# 情况2：企业从未选中变为选中（0→1）
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elif original_ent_selected == 0 and current_ent_selected == 1:
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ent = ents[idx - start]
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# 初始化产能
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if ent == 0:
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max_cap = self.utils.risk.C0_i_std[i]
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else:
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supplier_id = ent - 1
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max_cap = self.utils.supplier.Cj_i_std[supplier_id][i]
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capacity_layer[idx] = random.uniform(1, max_cap)
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# 初始化数量
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qi = self.utils.order.Q[i]
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if ent == 0:
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max_q = qi
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else:
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supplier_id = ent - 1
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max_q = self.utils.supplier.MaxOrder[supplier_id][i]
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quantity_layer[idx] = random.uniform(1, max_q)
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# 情况3：企业保持选中（1→1）或保持未选中（0→0）
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# 保持不变，后续的变异步骤会处理
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start = end
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# ========== 第四步：能力层变异（仅对保持选中的企业） ==========
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start = 0
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for i in range(self.utils.I):
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end = split_points[i]
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ents = self.utils.material_optional_enterprises[i]
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e_segment = enterprise_layer[start:end]
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for idx in range(start, end):
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# 仅对被选中的企业进行变异
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if random.random() < self.config.mutation_prob and e_segment[idx - start] == 1:
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# 只对保持选中的企业进行变异（1→1的情况）
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if e_segment[idx - start] == 1 and original_enterprise_layer[idx] == 1:
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if random.random() < self.config.mutation_prob:
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ent = ents[idx - start]
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qi = self.utils.order.Q[i] # 当前物料的总需求
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# 确定该企业的最大可分配数量
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if ent == 0:
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max_q = qi # 风险企业最多分配全部需求
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max_cap = self.utils.risk.C0_i_std[i]
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else:
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supplier_id = ent - 1
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max_q = self.utils.supplier.MaxOrder[supplier_id][i] # 供应商的最大供应量
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# 随机生成1到max_q之间的新数量
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quantity_layer[idx] = random.uniform(1, max_q)
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max_cap = self.utils.supplier.Cj_i_std[supplier_id][i]
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capacity_layer[idx] = random.uniform(1, max_cap)
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start = end
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# 修复数量层（确保总数量满足需求，且不超过企业最大供应）
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# 修复能力层（考虑所有选中企业的产能）
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capacity_layer = self.utils.repair_capacity_layer(enterprise_layer, capacity_layer)
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# ========== 第五步：数量层变异（仅对保持选中的企业） ==========
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start = 0
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for i in range(self.utils.I):
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end = split_points[i]
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ents = self.utils.material_optional_enterprises[i]
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e_segment = enterprise_layer[start:end]
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for idx in range(start, end):
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# 只对保持选中的企业进行变异（1→1的情况）
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if e_segment[idx - start] == 1 and original_enterprise_layer[idx] == 1:
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if random.random() < self.config.mutation_prob:
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ent = ents[idx - start]
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qi = self.utils.order.Q[i]
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if ent == 0:
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max_q = qi
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else:
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supplier_id = ent - 1
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max_q = self.utils.supplier.MaxOrder[supplier_id][i]
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quantity_layer[idx] = random.uniform(1, max_q)
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start = end
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# 修复数量层（考虑所有选中企业的分配）
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quantity_layer = self.utils.repair_quantity_layer(enterprise_layer, quantity_layer)
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# 合并三层并返回
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# 合并三层
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mutated = self.utils._merge_chromosome(enterprise_layer, capacity_layer, quantity_layer)
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return mutated
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