修改了评价指标，新的输入案例
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@ -2,76 +2,88 @@
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class OrderData:
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"""订单数据类：存储物料需求、交货期、成本等信息"""
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def __init__(self):
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self.I = 5 # 物料种类数
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self.Q = [6000, 12000, 20000, 7500, 13500] # 各物料的需求数量（整数）
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self.Dd = 30 # 需求交货期（单位：时间，整数）
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self.P0 = [45, 30, 30, 50, 40] # 风险企业的单位采购价（整数）
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self.T0 = [5, 8, 6, 7, 9] # 风险企业的单位运输成本（整数）
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self.transport_speed = 10 # 运输速度（单位：距离/时间，整数）
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self.I = 8 # 物料种类数
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self.Q = [6000, 12000, 22000, 7500, 13500, 16000, 8000, 14000] # 各物料的需求数量
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self.Dd = 40 # 需求交货期（单位：时间）
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self.P0 = [45, 30, 30, 50, 40, 45, 30, 30] # 风险企业的单位采购价
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self.T0 = [5, 8, 6, 7, 9, 4, 6, 7] # 风险企业的单位运输成本
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self.transport_speed = 10 # 运输速度（单位：距离/时间）
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class RiskEnterpriseData:
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"""风险企业数据类：存储风险企业的产能、距离等信息"""
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def __init__(self):
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self.I = 5 # 物料种类数（与订单一致）
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self.C0_i_min = [50, 100, 150, 80, 100] # 单物料的单位时间最小产能（整数）
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self.C0_total_max = 900 # 总产能上限（单位时间，整数）
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self.distance = 20 # 与需求点的距离（整数）
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self.I = 8 # 物料种类数（与订单一致）
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self.C0_i_min = [50, 100, 150, 80, 100, 150, 80, 100] # 单物料的单位时间最小产能
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self.C0_total_max = 18000 # 总产能上限（单位时间）
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self.distance = 30 # 与需求点的距离
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class SupplierData:
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"""供应商数据类：存储各供应商的产能、价格、距离等信息"""
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def __init__(self, I=5):
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def __init__(self, I=8):
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self.I = I # 物料种类数
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self.supplier_count = 4 # 供应商数量
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self.names = ["S0", "S1", "S2", "S3"] # 供应商名称
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self.supplier_count = 6 # 供应商数量
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self.names = ["S0", "S1", "S2", "S3", "S4", "S5"] # 供应商名称
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# 能否生产某物料的矩阵（supplier_count × I），1=能生产，0=不能
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self.can_produce = [
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[1, 1, 1, 1, 1],
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[1, 0, 1, 0, 1],
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[0, 1, 0, 1, 0],
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[0, 0, 1, 1, 1]
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[1, 1, 0, 1, 1, 0, 1, 1],
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[1, 0, 1, 0, 1, 1, 0, 1],
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[0, 1, 0, 1, 0, 0, 1, 0],
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[0, 1, 0, 1, 1, 1, 1, 1],
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[1, 1, 0, 1, 0, 0, 1, 0],
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[0, 0, 1, 0, 1, 0, 1, 1]
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]
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# 单物料单位时间最小产能（supplier_count × I），0表示不能生产该物料（整数）
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# 单物料单位时间最小产能（supplier_count × I），0表示不能生产该物料
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self.Cj_i_min = [
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[30, 80, 100, 60, 80],
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[60, 0, 180, 0, 120],
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[0, 150, 0, 120, 0],
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[0, 0, 170, 105, 115]
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[30, 80, 0, 60, 80, 0, 80, 90],
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[35, 0, 120, 0, 90, 110, 0, 110],
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[0, 70, 0, 70, 0, 0, 85, 0],
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[0, 75, 0, 75, 85, 95, 90, 100],
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[25, 60, 0, 80, 0, 0, 90, 0],
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[0, 0, 150, 0, 100, 0, 100, 95]
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]
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# 供应商单位时间的最大总产能（supplier_count，整数）
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self.Cj_total_max = [700, 800, 600, 850]
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# 最小起订量（supplier_count × I，整数）
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# 供应商单位时间的最大总产能（supplier_count）
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self.Cj_total_max = [1100, 950, 850, 1350, 750, 1000]
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# 最小起订量（supplier_count × I）
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self.MinOrder = [
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[800, 1500, 3000, 800, 1500],
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[1000, 0, 3500, 0, 1800],
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[0, 1700, 0, 1000, 0],
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[0, 0, 2500, 500, 1000]
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[300, 800, 0, 600, 800, 0, 800, 900],
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[350, 0, 1200, 0, 900, 1100, 0, 1100],
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[0, 700, 0, 700, 0, 0, 850, 0],
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[0, 750, 0, 750, 850, 950, 900, 1000],
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[250, 600, 0, 800, 0, 0, 900, 0],
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[0, 0, 1500, 0, 1000, 0, 1000, 950]
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]
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# 最大供应量（supplier_count × I，整数）
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# 最大供应量（supplier_count × I）
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self.MaxOrder = [
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[5000, 10000, 18000, 6500, 11000],
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[8000, 0, 25000, 0, 15000],
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[0, 8000, 0, 6000, 0],
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[0, 0, 20000, 7500, 13500]
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[3000, 8000, 0, 6000, 8000, 0, 8000, 9000],
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[3500, 0, 12000, 0, 9000, 11000, 0, 11000],
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[0, 7000, 0, 7000, 0, 0, 8500, 0],
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[0, 7500, 0, 7500, 8500, 9500, 9000, 7000],
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[3000, 6000, 0, 8000, 0, 0, 9000, 0],
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[0, 0, 15000, 0, 8000, 0, 6500, 9500]
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]
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# 单位采购价格（supplier_count × I，整数）
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# 单位采购价格（supplier_count × I）
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self.P_ij = [
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[50, 35, 28, 47, 38],
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[43, 0, 28, 0, 36],
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[0, 31, 0, 52, 0],
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[0, 0, 32, 52, 43]
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[50, 32, 0, 60, 42, 0, 32, 33],
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[55, 0, 33, 0, 44, 48, 0, 36],
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[0, 33, 0, 62, 0, 0, 32, 0],
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[0, 36, 0, 59, 46, 40, 34, 40],
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[53, 37, 0, 58, 0, 0, 39, 0],
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[0, 0, 32, 0, 43, 0, 35, 38]
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]
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# 单位运输成本（supplier_count × I，整数）
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# 单位运输成本（supplier_count × I）
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self.T_ij = [
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[6, 9, 8, 9, 12],
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[4, 0, 5, 0, 15],
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[0, 10, 0, 7, 0],
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[0, 0, 8, 9, 11]
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[8, 8, 0, 5, 15, 0, 8, 11],
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[13, 0, 8, 0, 13, 8, 0, 13],
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[0, 10, 0, 9, 0, 0, 11, 0],
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[0, 6, 0, 8, 11, 7, 9, 10],
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[4, 12, 0, 12, 0, 0, 12, 0],
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[0, 0, 10, 0, 12, 0, 8, 16]
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]
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# 供应商与需求点的距离（supplier_count，整数）
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self.distance = [60, 50, 70, 40]
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# 供应商与需求点的距离（supplier_count）
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self.distance = [50, 40, 60, 30, 60, 80, 50, 60]
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class Config:
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"""算法参数配置类：存储NSGA-II的各类参数"""
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def __init__(self):
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# 种群参数
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self.pop_size = 100 # 种群大小
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self.pop_size = 200 # 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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@ -87,26 +99,23 @@ class Config:
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self.early_stop_threshold = 0.15 # 目标值变化阈值
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# 目标函数数量
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self.objective_num = 2 # 双目标（成本+延期）
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self.duplicate_threshold = 0.05 # 重复解保留数量
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self.duplicate_threshold = 0.01 # 重复解保留数量
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self.print_top_n = 10 # 打印前N个最优解
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class DataStructures:
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"""数据结构工具类：提供评价指标计算等功能"""
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@staticmethod
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def calculate_evaluation_index(objectives, optimal_cost, optimal_tardiness):
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def calculate_evaluation_index(objectives, optimal_cost, optimal_tardiness, max_cost, max_tardiness):
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"""
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计算评价指标
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:param objectives: 解的目标值 (成本, 延期)
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:param optimal_cost: 最优成本值
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:param optimal_tardiness: 最优延期值
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: objectives: 解的目标值 (成本, 延期)
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: optimal_cost: 最优成本值
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: optimal_tardiness: 最优延期值
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: max_cost: 最大成本值
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: max_tardiness: 最大延期值
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:return: 评价指标值
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"""
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cost, tardiness = objectives
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# 避免除以零（成本最优值为0时的保护）
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if optimal_cost == 0:
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cost_ratio = cost
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else:
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cost_ratio = 2*(cost / optimal_cost)
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# 延期处理（+1避免除以零）
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tardiness_ratio = 800*((tardiness + 1) / (optimal_tardiness + 1))
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cost_ratio = cost/(max_cost - optimal_cost)
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tardiness_ratio = tardiness/( max_tardiness - optimal_tardiness)
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return cost_ratio + tardiness_ratio
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4
main.py
4
main.py
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@ -186,12 +186,14 @@ def main():
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# 找出各目标的最优值
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optimal_cost = min(obj[0] for obj in unique_front_objs)
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optimal_tardiness = min(obj[1] for obj in unique_front_objs)
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max_cost = max(obj[0] for obj in unique_front_objs)
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max_tardiness = max(obj[1] for obj in unique_front_objs)
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# 计算每个解的评价指标
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evaluated_solutions = []
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for sol, obj in zip(unique_front, unique_front_objs):
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index = DataStructures.calculate_evaluation_index(
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obj, optimal_cost, optimal_tardiness
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obj, optimal_cost, optimal_tardiness, max_cost, max_tardiness
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)
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evaluated_solutions.append((sol, obj, index))
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