272 lines
14 KiB
Python
272 lines
14 KiB
Python
import random
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import numpy as np
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from data_structures import OrderData, RiskEnterpriseData, SupplierData # 数据结构类
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class ChromosomeUtils:
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"""染色体工具类：提供染色体的拆分、合并、修复等功能，确保满足约束条件"""
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def __init__(self, order_data: OrderData, risk_data: RiskEnterpriseData, supplier_data: SupplierData):
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"""
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初始化工具类
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:param order_data: 订单数据（需求、交货期等）
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:param risk_data: 风险企业数据（产能等）
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:param supplier_data: 供应商数据（产能、价格等）
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"""
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self.order = order_data # 订单数据
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self.risk = risk_data # 风险企业数据
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self.supplier = supplier_data # 供应商数据
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self.I = order_data.I # 物料种类数
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# 预计算每个物料的可选企业列表（0=风险企业，1+为供应商ID）
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self.material_optional_enterprises = self._get_material_optional_enterprises()
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# 每个物料的可选企业数量
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self.material_enterprise_count = [len(ents) for ents in self.material_optional_enterprises]
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# 染色体总长度（3层：企业层+能力层+数量层）
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self.chromosome_length = 3 * sum(self.material_enterprise_count)
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def _get_material_optional_enterprises(self) -> list[list[int]]:
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"""
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生成每个物料的可选企业列表（内部方法）
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:return: 二维列表，每个子列表为对应物料的可选企业ID
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"""
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optional = []
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for i in range(self.I): # 遍历每种物料
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ents = [0] # 先加入风险企业（ID=0）
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# 加入可生产该物料的供应商（ID=1+供应商索引）
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for j in range(self.supplier.supplier_count):
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if self.supplier.can_produce[j][i] == 1: # 供应商j可生产物料i
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ents.append(j + 1) # 供应商ID为j+1（区分风险企业）
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optional.append(ents)
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return optional
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def _split_chromosome(self, chromosome: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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拆分染色体为三层
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:param chromosome: 完整染色体
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:return: 企业层、能力层、数量层（均为numpy数组）
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"""
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total_ent_count = sum(self.material_enterprise_count) # 所有物料的企业总数
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enterprise_layer = chromosome[:total_ent_count] # 第一层：企业选择（0/1）
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capacity_layer = chromosome[total_ent_count:2 * total_ent_count] # 第二层：产能
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quantity_layer = chromosome[2 * total_ent_count:] # 第三层：数量
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return enterprise_layer, capacity_layer, quantity_layer
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def _merge_chromosome(self, enterprise_layer: np.ndarray, capacity_layer: np.ndarray,
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quantity_layer: np.ndarray) -> np.ndarray:
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"""
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合并三层为完整染色体
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:param enterprise_layer: 企业层
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:param capacity_layer: 能力层
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:param quantity_layer: 数量层
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:return: 完整染色体
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"""
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return np.hstack([enterprise_layer, capacity_layer, quantity_layer])
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def repair_enterprise_layer(self, enterprise_layer: np.ndarray) -> np.ndarray:
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"""
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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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:param enterprise_layer: 待修复的企业层
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:return: 修复后的企业层
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"""
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repaired = enterprise_layer.copy()
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# 计算各物料的企业编码分割点
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split_points = np.cumsum(self.material_enterprise_count)
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start = 0 # 起始索引
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for i in range(self.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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segment = repaired[start:end] # 当前物料的企业选择片段
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if np.sum(segment) == 0: # 未选择任何企业时，随机选一个
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select_idx = random.randint(0, len(segment) - 1)
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segment[select_idx] = 1
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repaired[start:end] = segment
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start = end # 更新起始索引
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return repaired
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def repair_capacity_layer(self, enterprise_layer: np.ndarray, capacity_layer: np.ndarray) -> np.ndarray:
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"""
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修复能力层：确保满足单物料产能约束和企业总产能约束
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:param enterprise_layer: 企业层（用于确定哪些企业被选中）
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:param capacity_layer: 待修复的能力层
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:return: 修复后的能力层
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"""
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repaired = capacity_layer.copy()
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split_points = np.cumsum(self.material_enterprise_count)
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start = 0
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# 1. 修复单物料产能约束（每个企业的单物料产能不超过上限）
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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] # 可选企业列表
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segment = repaired[start:end] # 当前物料的产能片段
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enterprise_segment = enterprise_layer[start:end] # 企业选择状态
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for idx, ent in enumerate(ents):
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if enterprise_segment[idx] == 1: # 仅处理被选中的企业
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# 确定该企业的单物料最大产能
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if ent == 0:
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max_cap = self.risk.C0_i_std[i] # 风险企业
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else:
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supplier_id = ent - 1
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max_cap = self.supplier.Cj_i_std[supplier_id][i] # 供应商
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# 若产能不合法，重置为1到max_cap之间的随机值
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if segment[idx] <= 0 or segment[idx] > max_cap:
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segment[idx] = random.uniform(1, max_cap)
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repaired[start:end] = segment
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start = end
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# 2. 修复企业总产能约束（企业所有物料的总产能不超过上限）
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# 先计算每个企业的当前总产能
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enterprise_total_capacity = {} # {企业ID: 总产能}
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start = 0
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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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enterprise_segment = enterprise_layer[start:end]
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capacity_segment = repaired[start:end]
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for idx, ent in enumerate(ents):
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if enterprise_segment[idx] == 1: # 仅统计选中的企业
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if ent not in enterprise_total_capacity:
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enterprise_total_capacity[ent] = 0
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enterprise_total_capacity[ent] += capacity_segment[idx]
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start = end
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# 调整超出总产能上限的企业（按比例缩放）
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for ent, total_cap in enterprise_total_capacity.items():
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# 确定企业的总产能上限
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if ent == 0:
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max_total_cap = self.risk.C0_total_max # 风险企业总产能上限
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else:
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supplier_id = ent - 1
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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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start = 0
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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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enterprise_segment = enterprise_layer[start:end]
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capacity_segment = repaired[start:end]
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for idx, e in enumerate(ents):
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if e == ent and enterprise_segment[idx] == 1:
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capacity_segment[idx] *= scale # 按比例缩小
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repaired[start:end] = capacity_segment
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start = end
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return repaired
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def repair_quantity_layer(self, enterprise_layer: np.ndarray, quantity_layer: np.ndarray) -> np.ndarray:
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"""修复数量编码层：满足总量需求、起订量和最大供应量约束"""
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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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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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enterprise_segment = enterprise_layer[start:end]
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selected_indices = np.where(enterprise_segment == 1)[0]
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if len(selected_indices) == 0:
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start = end
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continue
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selected_ents = [ents[idx] for idx in selected_indices]
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# 步骤1：初始化合法范围（起订量~最大供应量）
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for idx, ent in zip(selected_indices, selected_ents):
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if ent == 0:
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min_q = 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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min_q = self.supplier.MinOrder[supplier_id][i]
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max_q = self.supplier.MaxOrder[supplier_id][i]
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if segment[idx] < min_q:
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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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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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weights /= np.sum(weights)
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segment[selected_indices] = qi * weights
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else:
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scale = qi / current_total
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segment[selected_indices] *= scale
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# 步骤3：处理超出最大供应量的情况（添加最大迭代次数限制）
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need_adjust = True
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max_iter = 10 # 最大迭代次数，避免无限循环
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iter_count = 0
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while need_adjust and iter_count < max_iter: # 增加迭代次数限制
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iter_count += 1
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need_adjust = False
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total_excess = 0
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# 截断超出max_q的数量
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for idx, ent in zip(selected_indices, selected_ents):
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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.supplier.MaxOrder[supplier_id][i]
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if segment[idx] > max_q:
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excess = segment[idx] - max_q
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total_excess += excess
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segment[idx] = max_q
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need_adjust = True
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# 分配超出量到有剩余的企业
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if total_excess > 0:
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available_indices = []
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available_max = []
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for idx, ent in zip(selected_indices, selected_ents):
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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.supplier.MaxOrder[supplier_id][i]
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remaining = max_q - segment[idx]
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if remaining > 0:
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available_indices.append(idx)
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available_max.append(remaining)
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if len(available_indices) > 0:
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available_total = sum(available_max)
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if available_total > 0:
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alloc_ratio = np.array(available_max) / available_total
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alloc_excess = total_excess * alloc_ratio
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for idx, alloc in zip(available_indices, alloc_excess):
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segment[idx] += alloc
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need_adjust = True # 分配后可能需要再次检查
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else:
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# 无剩余容量，强制截断并重新缩放总量
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segment[selected_indices] = np.minimum(segment[selected_indices],
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[self.supplier.MaxOrder[ent - 1][i] if ent != 0 else qi
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for ent in selected_ents])
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need_adjust = True
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# 检查总量误差，重新缩放
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current_total = np.sum(segment[selected_indices])
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if abs(current_total - qi) > 1e-6:
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scale = qi / current_total
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segment[selected_indices] *= scale
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need_adjust = True # 缩放后可能需要再次检查
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# 最终强制总量等于需求（避免迭代次数到限后仍有误差）
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current_total = np.sum(segment[selected_indices])
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if abs(current_total - qi) > 1e-6:
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scale = qi / current_total
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segment[selected_indices] *= scale
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repaired[start:end] = segment
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start = end
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return repaired
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def repair_chromosome(self, chromosome: np.ndarray) -> np.ndarray:
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"""
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完整修复染色体（按顺序修复三层）
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:param chromosome: 待修复的染色体
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:return: 修复后的染色体
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"""
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# 拆分三层
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enterprise_layer, capacity_layer, quantity_layer = self._split_chromosome(chromosome)
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# 依次修复（企业层→能力层→数量层，依赖关系）
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enterprise_layer = self.repair_enterprise_layer(enterprise_layer)
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capacity_layer = self.repair_capacity_layer(enterprise_layer, capacity_layer)
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quantity_layer = self.repair_quantity_layer(enterprise_layer, quantity_layer)
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# 合并修复后的三层
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return self._merge_chromosome(enterprise_layer, capacity_layer, quantity_layer) |