摘要
文中基于实际越库配送中零售商的多样化需求和集配过程的连续型,针对集配协同下的多产品车辆路径问题,构建以车辆固定成本、运输成本、时间窗惩罚成本和库存持有成本最小化为目标的带越库配送的车辆路径优化模型.根据问题的阶段性特征,提出一种改进的遗传算法对问题进行求解,并以车辆等待时间最小为准则设计解码方案.通过算例的对比分析,验证了改进的遗传算法有更强的寻优能力.结果表明:建立的模型能够有效降低总成本,提高运输效率.
Based on the diversified needs of retailers and the continuity of the collection and distribution process in the actual cross-warehouse distribution,aiming at the multi-product vehicle routing problem under the coordination of collection and distribution,an optimization model of vehicle routing with cross-warehouse distribution was established,which aimed at minimizing the fixed cost,transportation cost,time window penalty cost and inventory holding cost.According to the stage characteristics of the problem,an improved genetic algorithm was proposed to solve the problem,and the decoding scheme was designed with the minimum waiting time of vehicles as the criterion.Through the comparative analysis of examples,it is verified that the improved genetic algorithm has stronger optimization ability.The results show that the established model can effectively reduce the total cost and improve the transportation efficiency.
作者
王长琼
杨畅
WANG Zhangqiong;YANG Chang(School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China)
出处
《武汉理工大学学报（交通科学与工程版）》
2024年第2期385-391,共7页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
关键词
越库配送
集配协同
车辆路径问题
改进遗传算法
cross-docking
collaborative collection and distribution
vehicle routing problem
improved genetic algorithm
作者简介
第一作者:王长琼(1967-),女,博士,教授,主要研究领域为物流与供应链管理。
