提供差异服务组合的前置仓网络选址与配送路径协同优化

Coordinated Network Location Allocation and Distribution Route Optimization of Preposed Warehouses Providing Differential Service Portfolios

末端配送一直是提升物流效率的热点领域之一,针对日益增长的个性化与多元化配送需求带来的压力,研究了前置仓满足客户自提与送货上门的差异化配送模式,设计了基于需求驱动的配送网络系统并建立模型进行优化,将末端配送点选址与客户配送路径的物流链进行整合。并设计了一种基于差异化邻域策略的混合变邻域搜索算法对模型进行求解。通过多个不同规模的算例实验验证了算法求解的有效性,结果表明,该算法具有较好的求解效率,可以有效降低末端物流配送成本,同时兼顾了客户满意度。

With the rapid development of the logistics distribution system,improving the efficiency of terminal delivery in the logistics chain and the speed of order delivery has become a key concern for logistics companies,while terminal delivery has always been one of the most hotly studied areas for logistics efficiency augmentation.In response to the increasing pressure brought by personalized and diversified delivery demands,in this paper,we look at the preposed warehouse mode to satisfy the differentiated customer demand for self-pickup and home delivery.Next,we design a demand-driven delivery network system and its mathematical model,which integrates the location of the terminal delivery points into the logistics chain of the customer delivery process.Then we propose a mixed variable neighborhood search algorithm based on the differential neighborhood strategy to solve the model.The effectiveness of the algorithm is verified through multiple numerical examples of different scales,and the results show that:(1)Designing a high-quality location selection algorithms can help classify customer points and cluster customer points of complex distribution patterns,and a high-quality step-by-step optimization and construction algorithm can improve the optimization efficiency of subsequent algorithm iterations;(2)Compared with traditional heuristic algorithms,the differential mixed variable neighborhood search algorithm for path optimization has good performance and certain advantages.(3)By adopting a differential delivery service mode and coordinating the optimization of preposed warehouses and delivery paths,we can effectively reduce operating cost and improve service level.The model proposed in this paper has led to significant cost reduction and efficiency improvement effect on improving logistics terminal delivery.In terms of customer satisfaction,it can perform customer stratification management based on the customer satisfaction model and optimize service methods by dynamically adjusting the number and location of preposed warehouses to improve the overall customer satisfaction.In terms of the location allocation of the preposed warehouse,it can subject warehouse capacity to dynamic management based on changes in customer demand points to improve the utilization rate of the warehouse and reduce delivery cost.In terms of path optimization,the hybrid heuristic algorithm adopted under the model can yield highly efficient and high-quality results.Researches in the future can examine cases with dynamic change in customer point coordinates and with uncertainty in customer demand to further improve the flexibility of terminal delivery services.