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作 者:范厚明 白雪 田攀俊 FAN Hou-ming;BAI Xue;TIAN Pan-jun(Transportation Engineering College,Dalian Maritime University,Dalian 116026,China)
机构地区:[1]大连海事大学交通运输工程学院,辽宁大连116026
出 处:《控制与决策》2023年第2期501-509,共9页Control and Decision
基 金:国家社科基金应急管理体系建设研究专项项目(20VYJ024)。
摘 要:针对集货需求可拆分的多越库中心库门分配及车辆路径协同优化问题,综合考虑多越库中心共同配送、集货需求可拆分、库内操作等因素,以车辆派遣成本、车辆油耗成本、库内叉车固定成本和运输成本以及时间窗惩罚成本之和最小化为目标,建立优化模型.根据问题特征,设计混合遗传算法求解.该算法在交叉变异中引入具有方向性的粒子群寻优,采取进化逆转和保留最优个体策略改善求解质量.通过多组算例验证算法的有效性,并分析配送模式以及车辆类型对配送方案制定的影响.结果表明,所提出模式能有效降低越库中心运营成本.研究成果不仅可以丰富越库配送模式下的车辆路径问题研究,也为多越库中心物流企业合理利用资源制定科学的配送方案提供理论依据.For the collaborative optimization of multi-depot vehicle routing problem with split pickup and cross-dock door assignment, considering joint distribution of the multiple cross-dock center, split pickup and cross-docking operation,an optimization model is established to minimize the sum of vehicle dispatching cost, vehicle fuel cost, fixed and transportation cost of forklift in the cross-dock center and time window penalty cost. According to the characteristics of the problem, the hybrid genetic algorithm is designed through adding the directed optimization method of the particle swarm algorithm, the evolutionary reversal and elitist retention strategies are also used to improve the solution quality.The validity of the model and algorithm is verified by multiple examples, the influence of distribution mode and vehicle type on the distribution scheme formulation are also analyzed. The results show that the proposed model can effectively reduce the cross-dock centers’ operation cost. The research results expand and enrich the research of the vehicle routing problem with cross-docking, and provide theoretical basis for logistics enterprises with multiple cross-centers to make use of resources efficiently and formulate the decision distribution scheme scientifically.
关 键 词:多越库中心 库门分配 集货需求可拆分 异型车辆 混合遗传算法
分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]
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