184 lines
8.8 KiB
Python
184 lines
8.8 KiB
Python
import numpy as np
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from data_structures import OrderData, RiskEnterpriseData, SupplierData, Config
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from chromosome_utils import ChromosomeUtils
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class ObjectiveCalculator:
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"""目标函数计算器：计算双目标值（变更成本 + 交付延期）"""
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def __init__(self, order_data: OrderData, risk_data: RiskEnterpriseData, supplier_data: SupplierData,
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utils: ChromosomeUtils, config: Config):
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"""
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初始化计算器
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:param order_data: 订单数据
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:param risk_data: 风险企业数据
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:param supplier_data: 供应商数据
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:param utils: 染色体工具类
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:param config: 算法配置（惩罚系数等）
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"""
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self.order = order_data
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self.risk = risk_data
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self.supplier = supplier_data
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self.utils = utils
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self.config = config
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# 预计算物料企业编码的分割点（提高效率）
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self.split_points = np.cumsum(utils.material_enterprise_count)
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def calculate_objectives(self, chromosome: np.ndarray) -> tuple[float, float]:
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"""
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计算双目标值
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:param chromosome: 染色体（解）
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:return: (变更成本C, 交付延期T)
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"""
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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(chromosome)
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# 计算变更成本和交付延期
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C = self._calculate_change_cost(enterprise_layer, capacity_layer, quantity_layer)
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T = self._calculate_tardiness(enterprise_layer, capacity_layer, quantity_layer)
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return C, T
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def _calculate_change_cost(self, enterprise_layer: np.ndarray, capacity_layer: np.ndarray,
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quantity_layer: np.ndarray) -> float:
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"""
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计算变更成本 C = C1 + C2 + C3 + C4
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- C1: 变更惩罚成本（使用供应商替代风险企业的惩罚）
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- C2: 采购成本差异（变更后 - 原始）
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- C3: 运输成本差异（变更后 - 原始）
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- C4: 提前交付惩罚成本
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"""
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C1 = 0.0
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C2 = 0.0
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C3 = 0.0
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C4 = 0.0
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# 原始成本（全部由风险企业生产时的成本）
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original_purchase_cost = sum(self.order.Q[i] * self.order.P0[i] for i in range(self.order.I))
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original_transport_cost = sum(self.order.Q[i] * self.order.T0[i] for i in range(self.order.I))
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# 变更后成本（当前解的成本）
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new_purchase_cost = 0.0
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new_transport_cost = 0.0
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risk_production = np.zeros(self.order.I) # 风险企业生产的数量
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supplier_production = np.zeros(self.order.I) # 供应商生产的数量
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start = 0
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for i in range(self.order.I): # 遍历每种物料
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end = self.split_points[i]
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ents = self.utils.material_optional_enterprises[i] # 可选企业
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e_segment = enterprise_layer[start:end] # 企业选择状态
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q_segment = quantity_layer[start:end] # 数量分配
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for idx, ent in enumerate(ents):
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if e_segment[idx] == 1: # 仅处理被选中的企业
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q = q_segment[idx] # 分配的数量
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if ent == 0: # 风险企业
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risk_production[i] += q
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new_purchase_cost += q * self.order.P0[i] # 采购成本
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new_transport_cost += q * self.order.T0[i] # 运输成本
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else: # 供应商
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supplier_id = ent - 1
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supplier_production[i] += q
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new_purchase_cost += q * self.supplier.P_ij[supplier_id][i] # 采购成本
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new_transport_cost += q * self.supplier.T_ij[supplier_id][i] # 运输成本
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start = end
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# 计算C1：变更惩罚成本（对供应商生产的部分收取惩罚）
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for i in range(self.order.I):
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# 惩罚系数×供应商生产数量×（风险企业的单位采购+运输成本）
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C1 += self.config.delta * supplier_production[i] * (self.order.P0[i] + self.order.T0[i])
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# 计算C2：采购成本差异（变更后 - 原始）
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C2 = new_purchase_cost - original_purchase_cost
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# 计算C3：运输成本差异（变更后 - 原始）
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C3 = new_transport_cost - original_transport_cost
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# 计算C4：提前交付惩罚成本（若实际交货期早于需求交货期）
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actual_delivery_time = self._calculate_actual_delivery_time(enterprise_layer, capacity_layer, quantity_layer)
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if actual_delivery_time < self.order.Dd:
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# 计算风险企业和供应商的交货时间
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risk_delivery = [] # 风险企业的各物料交货时间
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supplier_deliveries = {} # {供应商ID: 各物料交货时间}
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start = 0
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for i in range(self.order.I):
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end = self.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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c_segment = capacity_layer[start:end] # 产能（用于计算生产时间）
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q_segment = quantity_layer[start:end] # 数量
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for idx, ent in enumerate(ents):
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if e_segment[idx] == 1:
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q = q_segment[idx]
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c = c_segment[idx]
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# 生产时间 = 数量 / 产能（产能为0时按0处理）
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production_time = q / c if c != 0 else 0
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# 运输时间 = 距离 / 运输速度
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if ent == 0:
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transport_time = self.risk.distance / self.order.transport_speed
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risk_delivery.append(production_time + transport_time)
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else:
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supplier_id = ent - 1
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transport_time = self.supplier.distance[supplier_id] / self.order.transport_speed
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if supplier_id not in supplier_deliveries:
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supplier_deliveries[supplier_id] = []
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supplier_deliveries[supplier_id].append(production_time + transport_time)
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start = end
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# 风险企业的最大交货时间（取最长）
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D0 = max(risk_delivery) if risk_delivery else 0
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# 所有供应商的最大交货时间之和
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Dj_sum = sum(max(times) for times in supplier_deliveries.values()) if supplier_deliveries else 0
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# 提前交付惩罚 = 惩罚系数 ×（需求交货期 - 风险企业交货时间 + 供应商总交货时间）
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C4 = self.config.gamma * ((self.order.Dd - D0) + Dj_sum)
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return C1 + C2 + C3 + C4
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def _calculate_actual_delivery_time(self, enterprise_layer: np.ndarray, capacity_layer: np.ndarray,
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quantity_layer: np.ndarray) -> float:
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"""
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计算实际交货期：所有企业中最长的（生产时间 + 运输时间）
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:return: 实际交货期
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"""
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max_time = 0.0 # 最大时间（实际交货期）
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start = 0
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for i in range(self.order.I):
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end = self.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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c_segment = capacity_layer[start:end] # 产能
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q_segment = quantity_layer[start:end] # 数量
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for idx, ent in enumerate(ents):
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if e_segment[idx] == 1: # 仅处理选中的企业
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q = q_segment[idx]
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c = c_segment[idx]
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# 生产时间 = 数量 / 产能（产能为0时按0处理）
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production_time = q / c if c != 0 else 0
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# 运输时间
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if ent == 0:
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transport_time = self.risk.distance / self.order.transport_speed
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else:
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supplier_id = ent - 1
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transport_time = self.supplier.distance[supplier_id] / self.order.transport_speed
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# 总时间 = 生产时间 + 运输时间
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total_time = production_time + transport_time
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if total_time > max_time:
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max_time = total_time # 更新最大时间
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start = end
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return max_time
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def _calculate_tardiness(self, enterprise_layer: np.ndarray, capacity_layer: np.ndarray,
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quantity_layer: np.ndarray) -> float:
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"""
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计算交付延期：max(0, 实际交货期 - 需求交货期)
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:return: 延期时间（非负）
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"""
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actual_delivery = self._calculate_actual_delivery_time(enterprise_layer, capacity_layer, quantity_layer)
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return max(0.0, actual_delivery - self.order.Dd) |