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import numpy as np
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import random
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from typing import List, Dict, Tuple, Optional
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import matplotlib.pyplot as plt
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# ============================= 数据输入模块（用户需填写）=============================
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class OrderData:
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"""订单基础数据类（对应文档表四7符号）"""
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def __init__(self):
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# 1. 订单核心参数
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self.I = 3 # 物料种类数（用户修改）
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self.Q = [100, 150, 200] # 物料i待生产量Qi（索引0对应物料1，用户修改）
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self.Dd = 10 # 订单需求交货期（时长，用户修改）
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# 2. 物料基础成本（风险企业供应）
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self.P = [50.0, 80.0, 60.0] # 物料i单位采购价格P_i（用户修改）
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self.T0 = [5.0, 8.0, 6.0] # 物料i风险企业运输成本T_i^0（用户修改）
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# 3. 惩罚系数（用户可按文档建议调整）
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self.alpha = 2.0 # C1增长系数
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self.beta = 1.0 # 时间尺度系数
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self.delta = 1.3 # 惩罚倍数（1.2-1.5）
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self.gamma = 0.5 # 提前交付惩罚系数
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class RiskEnterpriseData:
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"""风险企业数据类（对应文档表四7符号）"""
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def __init__(self):
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self.C0_max = 30.0 # 风险企业单位时间最大生产能力C0^max（用户修改）
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self.C0_std = 20.0 # 风险企业单位时间标准生产能力（用户修改）
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self.L1 = 3.0 # 扰动持续时长系数下限（关键订单3，一般订单2，用户修改）
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self.L2 = 10.0 # 扰动持续时长系数上限（关键订单10，一般订单5，用户修改）
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self.distance = 10.0 # 风险企业位置距离（用户修改）
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class SupplierData:
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"""供应商数据类（对应文档表四7符号）"""
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def __init__(self, order: OrderData):
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self.I = order.I
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# Ji：物料i的供应商个数（索引0对应物料1，用户修改）
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self.J = [2, 3, 2] # 物料1有2个供应商，物料2有3个，物料3有2个
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# 供应商j的基础参数（按物料i分组，用户修改）
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# 结构：self.suppliers[i][j] = {参数}，i=物料索引，j=供应商索引（0开始）
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self.suppliers = [
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# 物料1的2个供应商
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[
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{"Cj_max": 40.0, "Cj_std": 30.0, "P_ij": 55.0, "T_ij": 7.0, "MinOrder": 20, "MaxOrder": 100,
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"distance": 15.0},
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{"Cj_max": 35.0, "Cj_std": 25.0, "P_ij": 52.0, "T_ij": 9.0, "MinOrder": 15, "MaxOrder": 90,
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"distance": 20.0}
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],
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# 物料2的3个供应商
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[
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{"Cj_max": 50.0, "Cj_std": 40.0, "P_ij": 85.0, "T_ij": 6.0, "MinOrder": 25, "MaxOrder": 150,
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"distance": 12.0},
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{"Cj_max": 45.0, "Cj_std": 35.0, "P_ij": 82.0, "T_ij": 8.0, "MinOrder": 20, "MaxOrder": 120,
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"distance": 18.0},
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{"Cj_max": 55.0, "Cj_std": 45.0, "P_ij": 88.0, "T_ij": 5.0, "MinOrder": 30, "MaxOrder": 160,
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"distance": 10.0}
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],
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# 物料3的2个供应商
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[
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{"Cj_max": 42.0, "Cj_std": 32.0, "P_ij": 65.0, "T_ij": 8.0, "MinOrder": 22, "MaxOrder": 200,
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"distance": 16.0},
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{"Cj_max": 38.0, "Cj_std": 28.0, "P_ij": 63.0, "T_ij": 10.0, "MinOrder": 18, "MaxOrder": 180,
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"distance": 22.0}
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]
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]
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class AlgorithmParams:
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"""算法参数类（默认值可修改）"""
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def __init__(self):
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self.N = 100 # 种群规模
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self.Pm = 0.05 # 变异概率
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self.Pc = 0.8 # 交叉概率
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self.MaxIter = 200 # 最大迭代次数
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self.tournament_size = 3 # 锦标赛选择规模
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# 种群比例 N1:N2:N3:N4 = 3:3:2:2
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self.N1_ratio = 0.3
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self.N2_ratio = 0.3
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self.N3_ratio = 0.2
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self.N4_ratio = 0.2
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# ============================= 辅助计算函数（文档对应函数）=============================
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def calculate_RNum(P_Num: float, A_Num: float) -> float:
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"""计算剩余待生产数量R_Num = A_Num - P_Num（文档1.2.2节）"""
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return max(0.0, A_Num - P_Num)
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def calculate_ResTime(DueTime: float, riskTime: float) -> float:
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"""计算剩余交货期限ResTime = DueTime - riskTime（文档1.2.2节）"""
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return max(0.0, DueTime - riskTime)
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def calculate_TimeLevel(Td: float, ResTime: float, L1: float, L2: float) -> str:
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"""计算扰动持续程度TimeLevel（文档1.2.2节）"""
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if ResTime == 0:
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return "长"
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ratio = Td / ResTime
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if ratio <= L1:
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return "短"
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elif ratio >= L2:
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return "长"
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else:
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return "中"
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def calculate_CapLevel(R_Num: float, ResTime: float, C: float) -> str:
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"""计算订单生产能力等级CapLevel（文档1.2.2节）"""
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if ResTime == 0:
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return "不足"
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capacity = C * ResTime # 总生产能力
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if capacity >= R_Num * 1.2: # 预留20%缓冲
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return "充足"
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elif capacity <= R_Num * 0.8: # 缺口20%以上
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return "不足"
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else:
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return "一般"
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# ============================= 染色体与种群类=============================
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class Chromosome:
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"""染色体类（三层编码结构）"""
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def __init__(self, order: OrderData, risk_ent: RiskEnterpriseData, supplier: SupplierData):
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self.order = order
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self.risk_ent = risk_ent
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self.supplier = supplier
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self.I = order.I
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self.J_list = supplier.J # 各物料的供应商个数
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# 三层编码初始化（随机生成基础结构）
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self.enterprise_layer: List[List[int]] = [] # 企业编码层：I×(Ji+1)，0/1
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self.capacity_layer: List[List[float]] = [] # 能力编码层：I×(Ji+1)，生产能力
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self.quantity_layer: List[List[float]] = [] # 数量编码层：I×(Ji+1)，分配数量
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for i in range(self.I):
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Ji = self.J_list[i]
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total_ents = Ji + 1 # 1个风险企业 + Ji个供应商
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# 企业编码层：随机生成0/1，确保至少有1个1
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ent_codes = [random.randint(0, 1) for _ in range(total_ents)]
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if sum(ent_codes) == 0:
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ent_codes[random.randint(0, total_ents - 1)] = 1 # 强制激活1个
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self.enterprise_layer.append(ent_codes)
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# 能力编码层：基于企业标准生产能力随机生成
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cap_codes = []
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for j in range(total_ents):
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if j == 0: # 风险企业
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max_cap = risk_ent.C0_max
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std_cap = risk_ent.C0_std
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else: # 供应商j-1（索引转换）
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max_cap = supplier.suppliers[i][j - 1]["Cj_max"]
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std_cap = supplier.suppliers[i][j - 1]["Cj_std"]
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# 在[0.5×std_cap, max_cap]范围内随机
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cap = random.uniform(0.5 * std_cap, max_cap)
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cap_codes.append(cap)
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self.capacity_layer.append(cap_codes)
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# 数量编码层：基于物料待生产量随机分配
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qty_codes = [0.0] * total_ents
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Qi = order.Q[i]
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selected_ents = [idx for idx, val in enumerate(ent_codes) if val == 1]
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if len(selected_ents) > 0:
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# 随机分配数量（确保总和=Qi）
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qtys = np.random.dirichlet(np.ones(len(selected_ents))) * Qi
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for idx, ent_idx in enumerate(selected_ents):
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qty_codes[ent_idx] = qtys[idx]
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self.quantity_layer.append(qty_codes)
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# 修复初始染色体（确保满足约束）
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self.repair()
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def repair(self):
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"""染色体修复机制（文档1.4.1节）"""
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# 步骤1：检查每类物料是否有供应方（物料生产约束）
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for i in range(self.I):
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ent_codes = self.enterprise_layer[i]
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if sum(ent_codes) == 0:
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# 随机激活1个企业
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active_idx = random.randint(0, len(ent_codes) - 1)
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self.enterprise_layer[i][active_idx] = 1
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# 步骤2：检查企业最大生产能力约束
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self._repair_capacity_constraint()
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# 步骤3：检查物料数量约束（总和=Qi，供应商起订量/最大供应量）
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self._repair_quantity_constraint()
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def _repair_capacity_constraint(self):
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"""修复生产能力约束（修复属性名不匹配问题）"""
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# 步骤1：修复风险企业生产能力（原逻辑不变）
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risk_total_cap = 0.0
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risk_assignments = [] # 存储(i, cap)：物料i分配给风险企业的能力
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for i in range(self.I):
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if self.enterprise_layer[i][0] == 1: # 风险企业被选中
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cap = self.capacity_layer[i][0]
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risk_total_cap += cap
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risk_assignments.append((i, cap))
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# 若超出最大产能，剔除最小能力的物料
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while risk_total_cap > self.risk_ent.C0_max and len(risk_assignments) > 0:
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risk_assignments.sort(key=lambda x: x[1])
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min_i, min_cap = risk_assignments.pop(0)
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self.enterprise_layer[min_i][0] = 0 # 取消选择风险企业
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self.capacity_layer[min_i][0] = 0.0
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self.quantity_layer[min_i][0] = 0.0
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risk_total_cap -= min_cap
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# 步骤2：修复各供应商生产能力（核心修复：J_list → J）
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for i in range(self.I): # 遍历每个物料i，找到该物料的所有供应商编码索引
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Ji = self.J_list[i] # 物料i的供应商个数（注：此处self.J_list是Chromosome类自身属性，无需修改）
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for j in range(1, Ji + 1): # j=1~Ji：物料i的供应商编码索引（对应供应商j-1）
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supplier_idx = j - 1 # 供应商在suppliers列表中的索引（0开始）
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supp_total_cap = 0.0
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supp_assignments = []
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# 遍历所有物料k，计算该供应商（j索引）在各物料上的总产能
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for k in range(self.I):
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# 核心修复：self.supplier.J_list[k] → self.supplier.J[k]
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if j >= len(self.enterprise_layer[k]) or supplier_idx >= self.supplier.J[k]:
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continue # 跳过无该供应商的物料，避免索引越界
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# 若物料k选择了该供应商（j索引），累加产能
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if self.enterprise_layer[k][j] == 1:
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cap = self.capacity_layer[k][j]
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supp_total_cap += cap
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supp_assignments.append((k, cap))
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# 若该供应商总产能超出最大产能，剔除最小能力的物料
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if len(supp_assignments) == 0:
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continue # 无物料分配给该供应商，跳过
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# 获取该供应商在当前物料k上的最大产能（修正：用第一个分配的物料k）
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first_k = supp_assignments[0][0]
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supp_max_cap = self.supplier.suppliers[first_k][supplier_idx]["Cj_max"]
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# 剔除超出产能的物料分配
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while supp_total_cap > supp_max_cap and len(supp_assignments) > 0:
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supp_assignments.sort(key=lambda x: x[1])
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min_k, min_cap = supp_assignments.pop(0)
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self.enterprise_layer[min_k][j] = 0 # 取消该物料对该供应商的选择
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self.capacity_layer[min_k][j] = 0.0
|
|
|
|
|
self.quantity_layer[min_k][j] = 0.0
|
|
|
|
|
supp_total_cap -= min_cap
|
|
|
|
|
|
|
|
|
|
def _repair_quantity_constraint(self):
|
|
|
|
|
"""修复物料数量约束（修复除以零错误）"""
|
|
|
|
|
for i in range(self.I):
|
|
|
|
|
Qi = self.order.Q[i]
|
|
|
|
|
ent_codes = self.enterprise_layer[i]
|
|
|
|
|
qty_codes = self.quantity_layer[i]
|
|
|
|
|
selected_ents = [idx for idx, val in enumerate(ent_codes) if val == 1]
|
|
|
|
|
current_total = sum(qty_codes)
|
|
|
|
|
|
|
|
|
|
# 调整总量至Qi（允许微小误差）
|
|
|
|
|
if abs(current_total - Qi) > 1e-6:
|
|
|
|
|
ratio = Qi / (current_total + 1e-10) # 避免初始总量为0时除以零
|
|
|
|
|
for idx in selected_ents:
|
|
|
|
|
qty_codes[idx] *= ratio
|
|
|
|
|
|
|
|
|
|
# 检查供应商起订量和最大供应量
|
|
|
|
|
Ji = self.J_list[i]
|
|
|
|
|
for j in range(1, Ji + 1): # 供应商j-1
|
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|
|
|
if ent_codes[j] == 1:
|
|
|
|
|
min_order = self.supplier.suppliers[i][j - 1]["MinOrder"]
|
|
|
|
|
max_order = self.supplier.suppliers[i][j - 1]["MaxOrder"]
|
|
|
|
|
qty = qty_codes[j]
|
|
|
|
|
|
|
|
|
|
if qty < min_order:
|
|
|
|
|
# 低于起订量：要么增加到起订量，要么取消选择
|
|
|
|
|
if sum(qty_codes) + (min_order - qty) <= Qi * 1.1: # 允许10%缓冲
|
|
|
|
|
qty_codes[j] = min_order
|
|
|
|
|
# 从其他选中企业中减少对应数量
|
|
|
|
|
surplus_reduce = min_order - qty
|
|
|
|
|
other_ents = [idx for idx in selected_ents if idx != j]
|
|
|
|
|
if other_ents:
|
|
|
|
|
sum_other_qty = sum(qty_codes[idx] for idx in other_ents)
|
|
|
|
|
if sum_other_qty > 1e-10: # 避免除以零
|
|
|
|
|
reduce_ratio = surplus_reduce / sum_other_qty
|
|
|
|
|
for idx in other_ents:
|
|
|
|
|
qty_codes[idx] *= (1 - reduce_ratio)
|
|
|
|
|
else:
|
|
|
|
|
# 其他企业无数量可减，直接取消当前供应商选择
|
|
|
|
|
ent_codes[j] = 0
|
|
|
|
|
qty_codes[j] = 0.0
|
|
|
|
|
selected_ents.remove(j)
|
|
|
|
|
else:
|
|
|
|
|
ent_codes[j] = 0
|
|
|
|
|
qty_codes[j] = 0.0
|
|
|
|
|
selected_ents.remove(j)
|
|
|
|
|
|
|
|
|
|
elif qty > max_order:
|
|
|
|
|
# 超过最大供应量：调整到最大值，剩余差额分配给其他企业
|
|
|
|
|
qty_codes[j] = max_order
|
|
|
|
|
surplus_add = qty - max_order # 此处原逻辑笔误：应为当前qty - max_order，即超出部分转为需补充的差额
|
|
|
|
|
other_ents = [idx for idx in selected_ents if idx != j]
|
|
|
|
|
if other_ents:
|
|
|
|
|
# 核心修复：检查other_ents的数量总和是否为零
|
|
|
|
|
sum_other_qty = sum(qty_codes[idx] for idx in other_ents)
|
|
|
|
|
if sum_other_qty > 1e-10: # 总和非零，按比例分配
|
|
|
|
|
add_ratio = surplus_add / sum_other_qty
|
|
|
|
|
for idx in other_ents:
|
|
|
|
|
qty_codes[idx] *= (1 + add_ratio)
|
|
|
|
|
else:
|
|
|
|
|
# 总和为零，直接将差额分配给第一个其他企业
|
|
|
|
|
target_ent = other_ents[0]
|
|
|
|
|
# 确保不超过目标企业的最大供应量（若为供应商）
|
|
|
|
|
if target_ent == 0: # 目标是风险企业（无最大供应量限制，仅需满足产能）
|
|
|
|
|
qty_codes[target_ent] += surplus_add
|
|
|
|
|
else: # 目标是供应商，需检查最大供应量
|
|
|
|
|
supp_max_order = self.supplier.suppliers[i][target_ent - 1]["MaxOrder"]
|
|
|
|
|
add_qty = min(surplus_add, supp_max_order - qty_codes[target_ent])
|
|
|
|
|
qty_codes[target_ent] += add_qty
|
|
|
|
|
# 若仍有剩余差额（目标企业已达最大供应量），递归处理
|
|
|
|
|
remaining_surplus = surplus_add - add_qty
|
|
|
|
|
if remaining_surplus > 1e-6:
|
|
|
|
|
other_ents.remove(target_ent)
|
|
|
|
|
if other_ents:
|
|
|
|
|
# 重新计算剩余差额分配
|
|
|
|
|
sum_other_qty_new = sum(qty_codes[idx] for idx in other_ents)
|
|
|
|
|
if sum_other_qty_new > 1e-10:
|
|
|
|
|
add_ratio_new = remaining_surplus / sum_other_qty_new
|
|
|
|
|
for idx in other_ents:
|
|
|
|
|
qty_codes[idx] *= (1 + add_ratio_new)
|
|
|
|
|
else:
|
|
|
|
|
# 继续分配给下一个企业
|
|
|
|
|
for next_ent in other_ents:
|
|
|
|
|
if remaining_surplus <= 1e-6:
|
|
|
|
|
break
|
|
|
|
|
if next_ent == 0:
|
|
|
|
|
qty_codes[next_ent] += remaining_surplus
|
|
|
|
|
remaining_surplus = 0
|
|
|
|
|
else:
|
|
|
|
|
supp_max = self.supplier.suppliers[i][next_ent - 1]["MaxOrder"]
|
|
|
|
|
add = min(remaining_surplus, supp_max - qty_codes[next_ent])
|
|
|
|
|
qty_codes[next_ent] += add
|
|
|
|
|
remaining_surplus -= add
|
|
|
|
|
|
|
|
|
|
# 最终确保总和=Qi（避免累积误差）
|
|
|
|
|
current_total = sum(qty_codes)
|
|
|
|
|
if abs(current_total - Qi) > 1e-6 and selected_ents:
|
|
|
|
|
ratio = Qi / (current_total + 1e-10) # 加微小值避免除以零
|
|
|
|
|
for idx in selected_ents:
|
|
|
|
|
qty_codes[idx] *= ratio
|
|
|
|
|
|
|
|
|
|
def calculate_fitness(self) -> Tuple[float, float]:
|
|
|
|
|
"""计算适应度（变更成本C，交付延期T）"""
|
|
|
|
|
C1 = self._calculate_C1()
|
|
|
|
|
C2 = self._calculate_C2()
|
|
|
|
|
C3 = self._calculate_C3()
|
|
|
|
|
C4 = self._calculate_C4()
|
|
|
|
|
C = C1 + C2 + C3 + C4
|
|
|
|
|
|
|
|
|
|
T = self._calculate_T()
|
|
|
|
|
return C, T
|
|
|
|
|
|
|
|
|
|
def _calculate_C1(self) -> float:
|
|
|
|
|
"""计算变更惩罚成本C1"""
|
|
|
|
|
# 简化：假设ResTime=Dd（风险识别时刻为0），R_Num=Qi
|
|
|
|
|
ResTime = self.order.Dd
|
|
|
|
|
total_C1 = 0.0
|
|
|
|
|
for i in range(self.I):
|
|
|
|
|
Qi = self.order.Q[i]
|
|
|
|
|
# 最大惩罚成本=δ*(采购成本+运输成本)*Qi
|
|
|
|
|
max_penalty = self.order.delta * (self.order.P[i] + self.order.T0[i]) * Qi
|
|
|
|
|
# 指数惩罚项
|
|
|
|
|
exp_penalty = self.order.delta * Qi * np.exp(self.order.alpha / (ResTime + self.order.beta))
|
|
|
|
|
total_C1 += min(max_penalty, exp_penalty)
|
|
|
|
|
return total_C1
|
|
|
|
|
|
|
|
|
|
def _calculate_C2(self) -> float:
|
|
|
|
|
"""计算采购变更成本C2"""
|
|
|
|
|
total_purchase_risk = 0.0 # 原风险企业采购成本
|
|
|
|
|
total_purchase_supp = 0.0 # 变更后供应商采购成本
|
|
|
|
|
for i in range(self.I):
|
|
|
|
|
# 风险企业采购成本
|
|
|
|
|
if self.enterprise_layer[i][0] == 1:
|
|
|
|
|
total_purchase_risk += self.quantity_layer[i][0] * self.order.P[i]
|
|
|
|
|
# 供应商采购成本
|
|
|
|
|
Ji = self.J_list[i]
|
|
|
|
|
for j in range(1, Ji + 1):
|
|
|
|
|
if self.enterprise_layer[i][j] == 1:
|
|
|
|
|
supp_p = self.supplier.suppliers[i][j - 1]["P_ij"]
|
|
|
|
|
total_purchase_supp += self.quantity_layer[i][j] * supp_p
|
|
|
|
|
return total_purchase_supp - total_purchase_risk
|
|
|
|
|
|
|
|
|
|
def _calculate_C3(self) -> float:
|
|
|
|
|
"""计算运输变更成本C3"""
|
|
|
|
|
total_trans_risk = 0.0 # 原风险企业运输成本
|
|
|
|
|
total_trans_supp = 0.0 # 变更后供应商运输成本
|
|
|
|
|
for i in range(self.I):
|
|
|
|
|
# 风险企业运输成本
|
|
|
|
|
if self.enterprise_layer[i][0] == 1:
|
|
|
|
|
total_trans_risk += self.quantity_layer[i][0] * self.order.T0[i]
|
|
|
|
|
# 供应商运输成本
|
|
|
|
|
Ji = self.J_list[i]
|
|
|
|
|
for j in range(1, Ji + 1):
|
|
|
|
|
if self.enterprise_layer[i][j] == 1:
|
|
|
|
|
supp_t = self.supplier.suppliers[i][j - 1]["T_ij"]
|
|
|
|
|
total_trans_supp += self.quantity_layer[i][j] * supp_t
|
|
|
|
|
return total_trans_supp - total_trans_risk
|
|
|
|
|
|
|
|
|
|
def _calculate_C4(self) -> float:
|
|
|
|
|
"""计算提前交付惩罚成本C4"""
|
|
|
|
|
delivery_dates = self._get_delivery_dates()
|
|
|
|
|
actual_delivery = max(delivery_dates)
|
|
|
|
|
advance = self.order.Dd - actual_delivery
|
|
|
|
|
return self.order.gamma * max(0.0, advance)
|
|
|
|
|
|
|
|
|
|
def _calculate_T(self) -> float:
|
|
|
|
|
"""计算交付延期T"""
|
|
|
|
|
delivery_dates = self._get_delivery_dates()
|
|
|
|
|
actual_delivery = max(delivery_dates)
|
|
|
|
|
delay = actual_delivery - self.order.Dd
|
|
|
|
|
return max(0.0, delay)
|
|
|
|
|
|
|
|
|
|
def _get_delivery_dates(self) -> List[float]:
|
|
|
|
|
"""计算各企业的交付日期（生产耗时+运输耗时）"""
|
|
|
|
|
delivery_dates = []
|
|
|
|
|
for i in range(self.I):
|
|
|
|
|
# 风险企业交付日期
|
|
|
|
|
if self.enterprise_layer[i][0] == 1:
|
|
|
|
|
qty = self.quantity_layer[i][0]
|
|
|
|
|
cap = self.capacity_layer[i][0]
|
|
|
|
|
if cap == 0:
|
|
|
|
|
prod_time = 0.0
|
|
|
|
|
else:
|
|
|
|
|
prod_time = qty / cap # 生产耗时
|
|
|
|
|
trans_time = self.risk_ent.distance / 10.0 # 简化运输耗时（距离/10）
|
|
|
|
|
delivery_dates.append(prod_time + trans_time)
|
|
|
|
|
# 供应商交付日期
|
|
|
|
|
Ji = self.J_list[i]
|
|
|
|
|
for j in range(1, Ji + 1):
|
|
|
|
|
if self.enterprise_layer[i][j] == 1:
|
|
|
|
|
qty = self.quantity_layer[i][j]
|
|
|
|
|
cap = self.capacity_layer[i][j]
|
|
|
|
|
if cap == 0:
|
|
|
|
|
prod_time = 0.0
|
|
|
|
|
else:
|
|
|
|
|
prod_time = qty / cap
|
|
|
|
|
trans_time = self.supplier.suppliers[i][j - 1]["distance"] / 10.0
|
|
|
|
|
delivery_dates.append(prod_time + trans_time)
|
|
|
|
|
|
|
|
|
|
# 新增：如果没有任何交付日期（空列表），返回一个默认值
|
|
|
|
|
if not delivery_dates:
|
|
|
|
|
# 返回一个很大的值表示无法交付，触发最大延期惩罚
|
|
|
|
|
return [float('inf')]
|
|
|
|
|
return delivery_dates
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Population:
|
|
|
|
|
"""种群类"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, order: OrderData, risk_ent: RiskEnterpriseData, supplier: SupplierData, params: AlgorithmParams):
|
|
|
|
|
self.order = order
|
|
|
|
|
self.risk_ent = risk_ent
|
|
|
|
|
self.supplier = supplier
|
|
|
|
|
self.params = params
|
|
|
|
|
self.N = params.N
|
|
|
|
|
self.population: List[Chromosome] = []
|
|
|
|
|
|
|
|
|
|
# 初始化种群（混合策略）
|
|
|
|
|
self._initialize_population()
|
|
|
|
|
|
|
|
|
|
def _initialize_population(self):
|
|
|
|
|
"""混合种群初始化（文档1.4.1节）"""
|
|
|
|
|
N1 = int(self.N * self.params.N1_ratio) # 变更成本最小导向
|
|
|
|
|
N2 = int(self.N * self.params.N2_ratio) # 交付延期最短导向
|
|
|
|
|
N3 = int(self.N * self.params.N3_ratio) # 风险企业导向
|
|
|
|
|
N4 = self.N - N1 - N2 - N3 # 随机种群
|
|
|
|
|
|
|
|
|
|
# 1. 生成N1：变更成本最小导向
|
|
|
|
|
self.population.extend(self._generate_target_oriented_pop(N1, target="cost"))
|
|
|
|
|
|
|
|
|
|
# 2. 生成N2：交付延期最短导向
|
|
|
|
|
self.population.extend(self._generate_target_oriented_pop(N2, target="delay"))
|
|
|
|
|
|
|
|
|
|
# 3. 生成N3：风险企业导向
|
|
|
|
|
self.population.extend(self._generate_risk_ent_oriented_pop(N3))
|
|
|
|
|
|
|
|
|
|
# 4. 生成N4：随机种群
|
|
|
|
|
self.population.extend([Chromosome(self.order, self.risk_ent, self.supplier) for _ in range(N4)])
|
|
|
|
|
|
|
|
|
|
def _generate_target_oriented_pop(self, size: int, target: str) -> List[Chromosome]:
|
|
|
|
|
"""生成目标函数导向种群（文档1.4.1节）"""
|
|
|
|
|
if size <= 0:
|
|
|
|
|
return []
|
|
|
|
|
|
|
|
|
|
# 随机生成2*size个候选解
|
|
|
|
|
candidates = [Chromosome(self.order, self.risk_ent, self.supplier) for _ in range(2 * size)]
|
|
|
|
|
|
|
|
|
|
# 计算适应度并排序
|
|
|
|
|
if target == "cost":
|
|
|
|
|
# 按变更成本升序排序
|
|
|
|
|
candidates.sort(key=lambda x: x.calculate_fitness()[0])
|
|
|
|
|
else: # target == "delay"
|
|
|
|
|
# 按交付延期升序排序
|
|
|
|
|
candidates.sort(key=lambda x: x.calculate_fitness()[1])
|
|
|
|
|
|
|
|
|
|
# 选择前size个
|
|
|
|
|
return candidates[:size]
|
|
|
|
|
|
|
|
|
|
def _generate_risk_ent_oriented_pop(self, size: int) -> List[Chromosome]:
|
|
|
|
|
"""生成风险企业导向种群（文档1.4.1节）"""
|
|
|
|
|
if size <= 0:
|
|
|
|
|
return []
|
|
|
|
|
|
|
|
|
|
candidates = []
|
|
|
|
|
for _ in range(2 * size):
|
|
|
|
|
chrom = Chromosome(self.order, self.risk_ent, self.supplier)
|
|
|
|
|
# 强制风险企业选中所有物料
|
|
|
|
|
for i in range(self.order.I):
|
|
|
|
|
chrom.enterprise_layer[i][0] = 1 # yi0=1
|
|
|
|
|
# 修复染色体
|
|
|
|
|
chrom.repair()
|
|
|
|
|
candidates.append(chrom)
|
|
|
|
|
|
|
|
|
|
# 计算Pareto Rank，选择Rank最低的size个
|
|
|
|
|
ranked_candidates = self._fast_non_dominated_sort(candidates)[0] # Rank=0的解
|
|
|
|
|
if len(ranked_candidates) >= size:
|
|
|
|
|
return random.sample(ranked_candidates, size)
|
|
|
|
|
else:
|
|
|
|
|
# 不足时补充其他Rank的解
|
|
|
|
|
all_ranked = self._fast_non_dominated_sort(candidates)
|
|
|
|
|
selected = []
|
|
|
|
|
for rank in all_ranked:
|
|
|
|
|
selected.extend(rank)
|
|
|
|
|
if len(selected) >= size:
|
|
|
|
|
return selected[:size]
|
|
|
|
|
return selected
|
|
|
|
|
|
|
|
|
|
def _fast_non_dominated_sort(self, population: List[Chromosome]) -> List[List[Chromosome]]:
|
|
|
|
|
"""快速非支配排序（标准NSGA-II算法）"""
|
|
|
|
|
dominated_sets = [[] for _ in range(len(population))]
|
|
|
|
|
domination_counts = [0] * len(population)
|
|
|
|
|
ranks = [0] * len(population)
|
|
|
|
|
fronts = [[]]
|
|
|
|
|
|
|
|
|
|
# 计算每个个体的支配关系
|
|
|
|
|
for p_idx in range(len(population)):
|
|
|
|
|
p_cost, p_delay = population[p_idx].calculate_fitness()
|
|
|
|
|
for q_idx in range(len(population)):
|
|
|
|
|
if p_idx == q_idx:
|
|
|
|
|
continue
|
|
|
|
|
q_cost, q_delay = population[q_idx].calculate_fitness()
|
|
|
|
|
|
|
|
|
|
# p支配q：p的两个目标都不劣于q，且至少一个目标更优
|
|
|
|
|
if (p_cost <= q_cost and p_delay <= q_delay) and (p_cost < q_cost or p_delay < q_delay):
|
|
|
|
|
dominated_sets[p_idx].append(q_idx)
|
|
|
|
|
# q支配p
|
|
|
|
|
elif (q_cost <= p_cost and q_delay <= p_delay) and (q_cost < p_cost or q_delay < q_delay):
|
|
|
|
|
domination_counts[p_idx] += 1
|
|
|
|
|
|
|
|
|
|
# 初始前沿（Rank=0）
|
|
|
|
|
if domination_counts[p_idx] == 0:
|
|
|
|
|
ranks[p_idx] = 0
|
|
|
|
|
fronts[0].append(population[p_idx])
|
|
|
|
|
|
|
|
|
|
# 生成后续前沿
|
|
|
|
|
rank = 1
|
|
|
|
|
while True:
|
|
|
|
|
current_front = []
|
|
|
|
|
for p_idx in range(len(population)):
|
|
|
|
|
if ranks[p_idx] == rank - 1:
|
|
|
|
|
for q_idx in dominated_sets[p_idx]:
|
|
|
|
|
domination_counts[q_idx] -= 1
|
|
|
|
|
if domination_counts[q_idx] == 0:
|
|
|
|
|
ranks[q_idx] = rank
|
|
|
|
|
current_front.append(population[q_idx])
|
|
|
|
|
if not current_front:
|
|
|
|
|
break
|
|
|
|
|
fronts.append(current_front)
|
|
|
|
|
rank += 1
|
|
|
|
|
|
|
|
|
|
return fronts
|
|
|
|
|
|
|
|
|
|
def _calculate_crowding_distance(self, front: List[Chromosome]) -> List[float]:
|
|
|
|
|
"""计算拥挤度（标准NSGA-II算法）"""
|
|
|
|
|
n = len(front)
|
|
|
|
|
if n == 0:
|
|
|
|
|
return []
|
|
|
|
|
distances = [0.0] * n
|
|
|
|
|
|
|
|
|
|
# 按变更成本排序
|
|
|
|
|
cost_sorted = sorted(enumerate(front), key=lambda x: x[1].calculate_fitness()[0])
|
|
|
|
|
# 边界个体拥挤度设为无穷大
|
|
|
|
|
distances[cost_sorted[0][0]] = float('inf')
|
|
|
|
|
distances[cost_sorted[-1][0]] = float('inf')
|
|
|
|
|
# 计算中间个体拥挤度
|
|
|
|
|
max_cost = cost_sorted[-1][1].calculate_fitness()[0]
|
|
|
|
|
min_cost = cost_sorted[0][1].calculate_fitness()[0]
|
|
|
|
|
if max_cost - min_cost > 1e-6:
|
|
|
|
|
for i in range(1, n - 1):
|
|
|
|
|
prev_cost = cost_sorted[i - 1][1].calculate_fitness()[0]
|
|
|
|
|
next_cost = cost_sorted[i + 1][1].calculate_fitness()[0]
|
|
|
|
|
distances[cost_sorted[i][0]] += (next_cost - prev_cost) / (max_cost - min_cost)
|
|
|
|
|
|
|
|
|
|
# 按交付延期排序
|
|
|
|
|
delay_sorted = sorted(enumerate(front), key=lambda x: x[1].calculate_fitness()[1])
|
|
|
|
|
# 边界个体拥挤度设为无穷大
|
|
|
|
|
distances[delay_sorted[0][0]] = float('inf')
|
|
|
|
|
distances[delay_sorted[-1][0]] = float('inf')
|
|
|
|
|
# 计算中间个体拥挤度
|
|
|
|
|
max_delay = delay_sorted[-1][1].calculate_fitness()[1]
|
|
|
|
|
min_delay = delay_sorted[0][1].calculate_fitness()[1]
|
|
|
|
|
if max_delay - min_delay > 1e-6:
|
|
|
|
|
for i in range(1, n - 1):
|
|
|
|
|
prev_delay = delay_sorted[i - 1][1].calculate_fitness()[1]
|
|
|
|
|
next_delay = delay_sorted[i + 1][1].calculate_fitness()[1]
|
|
|
|
|
distances[delay_sorted[i][0]] += (next_delay - prev_delay) / (max_delay - min_delay)
|
|
|
|
|
|
|
|
|
|
return distances
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# ============================= 遗传操作函数=============================
|
|
|
|
|
def crossover(parent1: Chromosome, parent2: Chromosome, Pc: float) -> Tuple[Chromosome, Chromosome]:
|
|
|
|
|
"""两点交叉（文档1.4.1节）"""
|
|
|
|
|
if random.random() > Pc:
|
|
|
|
|
return parent1, parent2
|
|
|
|
|
|
|
|
|
|
# 复制父代染色体
|
|
|
|
|
child1 = Chromosome(parent1.order, parent1.risk_ent, parent1.supplier)
|
|
|
|
|
child2 = Chromosome(parent2.order, parent2.risk_ent, parent2.supplier)
|
|
|
|
|
|
|
|
|
|
# 确定染色体总基因长度（每层基因数相同）
|
|
|
|
|
total_genes_per_layer = sum([len(layer) for layer in parent1.enterprise_layer])
|
|
|
|
|
if total_genes_per_layer < 2:
|
|
|
|
|
return child1, child2
|
|
|
|
|
|
|
|
|
|
# 随机选择两个交叉位置
|
|
|
|
|
pos1 = random.randint(0, total_genes_per_layer - 2)
|
|
|
|
|
pos2 = random.randint(pos1 + 1, total_genes_per_layer - 1)
|
|
|
|
|
|
|
|
|
|
# 对三层编码分别执行交叉
|
|
|
|
|
for layer_idx, (layer1, layer2) in enumerate([
|
|
|
|
|
(child1.enterprise_layer, child2.enterprise_layer),
|
|
|
|
|
(child1.capacity_layer, child2.capacity_layer),
|
|
|
|
|
(child1.quantity_layer, child2.quantity_layer)
|
|
|
|
|
]):
|
|
|
|
|
# 展平层编码
|
|
|
|
|
flat1 = []
|
|
|
|
|
flat2 = []
|
|
|
|
|
for segment in layer1:
|
|
|
|
|
flat1.extend(segment)
|
|
|
|
|
for segment in layer2:
|
|
|
|
|
flat2.extend(segment)
|
|
|
|
|
|
|
|
|
|
# 交换pos1-pos2区间的基因
|
|
|
|
|
flat1[pos1:pos2 + 1], flat2[pos1:pos2 + 1] = flat2[pos1:pos2 + 1], flat1[pos1:pos2 + 1]
|
|
|
|
|
|
|
|
|
|
# 重构层编码
|
|
|
|
|
ptr = 0
|
|
|
|
|
for i in range(len(layer1)):
|
|
|
|
|
segment_len = len(layer1[i])
|
|
|
|
|
layer1[i] = flat1[ptr:ptr + segment_len]
|
|
|
|
|
layer2[i] = flat2[ptr:ptr + segment_len]
|
|
|
|
|
ptr += segment_len
|
|
|
|
|
|
|
|
|
|
# 修复子代染色体
|
|
|
|
|
child1.repair()
|
|
|
|
|
child2.repair()
|
|
|
|
|
|
|
|
|
|
return child1, child2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def mutate(chrom: Chromosome, Pm: float) -> Chromosome:
|
|
|
|
|
"""均匀变异（文档1.4.1节）"""
|
|
|
|
|
# 对三层编码分别执行变异
|
|
|
|
|
# 1. 企业编码层（0/1翻转）
|
|
|
|
|
for i in range(chrom.I):
|
|
|
|
|
for j in range(len(chrom.enterprise_layer[i])):
|
|
|
|
|
if random.random() < Pm:
|
|
|
|
|
chrom.enterprise_layer[i][j] = 1 - chrom.enterprise_layer[i][j]
|
|
|
|
|
|
|
|
|
|
# 2. 能力编码层（随机重置）
|
|
|
|
|
for i in range(chrom.I):
|
|
|
|
|
Ji = chrom.J_list[i]
|
|
|
|
|
for j in range(len(chrom.capacity_layer[i])):
|
|
|
|
|
if random.random() < Pm:
|
|
|
|
|
if j == 0: # 风险企业
|
|
|
|
|
max_cap = chrom.risk_ent.C0_max
|
|
|
|
|
std_cap = chrom.risk_ent.C0_std
|
|
|
|
|
else: # 供应商j-1
|
|
|
|
|
max_cap = chrom.supplier.suppliers[i][j - 1]["Cj_max"]
|
|
|
|
|
std_cap = chrom.supplier.suppliers[i][j - 1]["Cj_std"]
|
|
|
|
|
chrom.capacity_layer[i][j] = random.uniform(0.5 * std_cap, max_cap)
|
|
|
|
|
|
|
|
|
|
# 3. 数量编码层（随机重置）
|
|
|
|
|
for i in range(chrom.I):
|
|
|
|
|
Qi = chrom.order.Q[i]
|
|
|
|
|
selected_ents = [idx for idx, val in enumerate(chrom.enterprise_layer[i]) if val == 1]
|
|
|
|
|
if len(selected_ents) > 0 and random.random() < Pm:
|
|
|
|
|
# 重新分配数量
|
|
|
|
|
qtys = np.random.dirichlet(np.ones(len(selected_ents))) * Qi
|
|
|
|
|
for idx, ent_idx in enumerate(selected_ents):
|
|
|
|
|
chrom.quantity_layer[i][ent_idx] = qtys[idx]
|
|
|
|
|
|
|
|
|
|
# 修复变异后的染色体
|
|
|
|
|
chrom.repair()
|
|
|
|
|
return chrom
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def tournament_selection(population: List[Chromosome], tournament_size: int) -> Chromosome:
|
|
|
|
|
"""锦标赛选择"""
|
|
|
|
|
candidates = random.sample(population, tournament_size)
|
|
|
|
|
# 选择Pareto支配最优的个体
|
|
|
|
|
best = candidates[0]
|
|
|
|
|
best_cost, best_delay = best.calculate_fitness()
|
|
|
|
|
for cand in candidates[1:]:
|
|
|
|
|
cand_cost, cand_delay = cand.calculate_fitness()
|
|
|
|
|
# cand支配best
|
|
|
|
|
if (cand_cost <= best_cost and cand_delay <= best_delay) and (cand_cost < best_cost or cand_delay < best_delay):
|
|
|
|
|
best = cand
|
|
|
|
|
best_cost, best_delay = cand_cost, cand_delay
|
|
|
|
|
return best
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# ============================= 主算法函数=============================
|
|
|
|
|
def improved_nsga2() -> Tuple[List[Chromosome], List[Dict]]:
|
|
|
|
|
"""改进NSGA-II算法主函数（文档1.4.2节流程）"""
|
|
|
|
|
# 1. 初始化数据和参数
|
|
|
|
|
order = OrderData()
|
|
|
|
|
risk_ent = RiskEnterpriseData()
|
|
|
|
|
supplier = SupplierData(order)
|
|
|
|
|
params = AlgorithmParams()
|
|
|
|
|
|
|
|
|
|
# 2. 初始化种群（第0代）
|
|
|
|
|
pop = Population(order, risk_ent, supplier, params)
|
|
|
|
|
population = pop.population
|
|
|
|
|
|
|
|
|
|
# 3. 迭代优化
|
|
|
|
|
iteration_history = [] # 记录每代最优目标值
|
|
|
|
|
for iter_idx in range(params.MaxIter):
|
|
|
|
|
# 4. 生成子代种群
|
|
|
|
|
offspring = []
|
|
|
|
|
while len(offspring) < params.N:
|
|
|
|
|
# 锦标赛选择父代
|
|
|
|
|
parent1 = tournament_selection(population, params.tournament_size)
|
|
|
|
|
parent2 = tournament_selection(population, params.tournament_size)
|
|
|
|
|
# 交叉
|
|
|
|
|
child1, child2 = crossover(parent1, parent2, params.Pc)
|
|
|
|
|
# 变异
|
|
|
|
|
child1 = mutate(child1, params.Pm)
|
|
|
|
|
child2 = mutate(child2, params.Pm)
|
|
|
|
|
# 添加到子代
|
|
|
|
|
offspring.append(child1)
|
|
|
|
|
if len(offspring) < params.N:
|
|
|
|
|
offspring.append(child2)
|
|
|
|
|
|
|
|
|
|
# 5. 合并父代和子代
|
|
|
|
|
combined = population + offspring
|
|
|
|
|
|
|
|
|
|
# 6. 快速非支配排序
|
|
|
|
|
fronts = pop._fast_non_dominated_sort(combined)
|
|
|
|
|
|
|
|
|
|
# 7. 拥挤度计算与精英保留
|
|
|
|
|
next_population = []
|
|
|
|
|
for front in fronts:
|
|
|
|
|
if len(next_population) + len(front) <= params.N:
|
|
|
|
|
# 该前沿全部入选
|
|
|
|
|
next_population.extend(front)
|
|
|
|
|
else:
|
|
|
|
|
# 计算拥挤度，选择拥挤度大的个体
|
|
|
|
|
distances = pop._calculate_crowding_distance(front)
|
|
|
|
|
# 按拥挤度降序排序
|
|
|
|
|
front_sorted = [front[i] for i in np.argsort(distances)[::-1]]
|
|
|
|
|
# 选择剩余名额
|
|
|
|
|
need = params.N - len(next_population)
|
|
|
|
|
next_population.extend(front_sorted[:need])
|
|
|
|
|
break
|
|
|
|
|
|
|
|
|
|
# 8. 更新种群
|
|
|
|
|
population = next_population
|
|
|
|
|
|
|
|
|
|
# 9. 记录迭代历史
|
|
|
|
|
current_front = pop._fast_non_dominated_sort(population)[0]
|
|
|
|
|
min_cost = min([chrom.calculate_fitness()[0] for chrom in current_front])
|
|
|
|
|
min_delay = min([chrom.calculate_fitness()[1] for chrom in current_front])
|
|
|
|
|
iteration_history.append({
|
|
|
|
|
"iter": iter_idx,
|
|
|
|
|
"min_cost": min_cost,
|
|
|
|
|
"min_delay": min_delay,
|
|
|
|
|
"pareto_size": len(current_front)
|
|
|
|
|
})
|
|
|
|
|
|
|
|
|
|
# 打印迭代信息
|
|
|
|
|
if (iter_idx + 1) % 20 == 0:
|
|
|
|
|
print(
|
|
|
|
|
f"迭代 {iter_idx + 1:3d} | 最优成本: {min_cost:8.2f} | 最优延期: {min_delay:6.2f} | Pareto解数: {len(current_front)}")
|
|
|
|
|
|
|
|
|
|
# 10. 输出最终Pareto解集
|
|
|
|
|
final_front = pop._fast_non_dominated_sort(population)[0]
|
|
|
|
|
return final_front, iteration_history
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# ============================= 结果可视化与输出=============================
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def visualize_results(pareto_front: List[Chromosome], iteration_history: List[Dict]):
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"""结果可视化"""
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# 1. Pareto前沿曲线
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costs = [chrom.calculate_fitness()[0] for chrom in pareto_front]
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delays = [chrom.calculate_fitness()[1] for chrom in pareto_front]
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plt.figure(figsize=(12, 5))
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# 子图1：Pareto前沿
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plt.subplot(1, 2, 1)
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plt.scatter(costs, delays, c='red', s=50, alpha=0.7)
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plt.xlabel('变更成本', fontsize=12)
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plt.ylabel('交付延期', fontsize=12)
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plt.title('Pareto最优前沿', fontsize=14)
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plt.grid(True, alpha=0.3)
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# 子图2：迭代收敛曲线
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plt.subplot(1, 2, 2)
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iters = [item["iter"] for item in iteration_history]
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min_costs = [item["min_cost"] for item in iteration_history]
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min_delays = [item["min_delay"] for item in iteration_history]
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plt.plot(iters, min_costs, label='最优变更成本', linewidth=2)
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plt.plot(iters, min_delays, label='最优交付延期', linewidth=2, linestyle='--')
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plt.xlabel('迭代次数', fontsize=12)
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plt.ylabel('目标值', fontsize=12)
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plt.title('迭代收敛曲线', fontsize=14)
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.show()
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def print_pareto_solutions(pareto_front: List[Chromosome]):
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"""打印Pareto解的详细信息"""
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print("\n" + "=" * 80)
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print("Pareto最优解集详细信息")
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print("=" * 80)
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for idx, chrom in enumerate(pareto_front):
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cost, delay = chrom.calculate_fitness()
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print(f"\n解 {idx + 1}:")
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print(f" 变更成本: {cost:.2f} | 交付延期: {delay:.2f}")
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print(" 订单分配方案:")
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for i in range(chrom.I):
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print(f" 物料{i + 1} (待生产: {chrom.order.Q[i]}):")
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# 风险企业分配
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if chrom.enterprise_layer[i][0] == 1:
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qty = chrom.quantity_layer[i][0]
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cap = chrom.capacity_layer[i][0]
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print(f" 风险企业: 数量={qty:.1f}, 生产能力={cap:.1f}")
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# 供应商分配
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Ji = chrom.J_list[i]
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for j in range(1, Ji + 1):
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if chrom.enterprise_layer[i][j] == 1:
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qty = chrom.quantity_layer[i][j]
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cap = chrom.capacity_layer[i][j]
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supp_info = chrom.supplier.suppliers[i][j - 1]
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print(f" 供应商{j}: 数量={qty:.1f}, 生产能力={cap:.1f}, 采购价={supp_info['P_ij']:.1f}")
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# ============================= 主函数调用=============================
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if __name__ == "__main__":
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# 运行算法
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print("改进NSGA-II算法开始运行...")
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print("=" * 60)
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pareto_front, iteration_history = improved_nsga2()
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# 结果输出与可视化
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print_pareto_solutions(pareto_front)
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visualize_results(pareto_front, iteration_history)
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