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Collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups

机译:具有时间窗口和混合交付和取货的协作式多中心车辆路线问题

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This study focuses on the collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups (CMVRPTWMDP), which is a variant of the vehicle routing problem (VRP) with mixed deliveries and pickups, and VRP with simultaneous deliveries and pickups and time windows. Collaboration and transportation resource sharing are adopted to optimize vehicle routes in the CMVRPTWMDP, to integrate the delivery and pickup services with time windows, and to construct open-closed mixed vehicle routes. First, the CMVRPTWMDP is formulated as a mixed-integer programming model to minimize logistics operating costs. The effect of transportation resource sharing on reducing the number of needed vehicles and the maintenance cost is considered in the model formulation. Second, a two-stage hybrid algorithm combining customer clustering and vehicle routing optimization is designed to solve the CMVRPTWMDP. An improved 3D k-means clustering algorithm based on space-time distances and the customer demand is proposed to reassign customers to logistics facilities (e.g., delivery or pickup centers). Furthermore, a hybrid heuristic algorithm that combines the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, called GA-PSO, is designed to optimize the vehicle routes. A coordination operator between GA and PSO is designed to allow particles and chromosomes to interact, increasing the diversity of particle swarms and the possibility of finding a feasible solution. Third, the performance and effectiveness of the proposed approach are tested by comparing them with the CPLEX solver using 30 small-scale instances and other existing algorithms using 25 benchmark instances. Fourth, the minimum costs-remaining savings (MCRS) model is adopted to design a fair and reasonable profit allocation scheme for participants in the collaborative alliance and maintain alliance stability. Finally, the optimization results of a real-world case study from Chongqing, China, show that transportation resource misuse and logistics operating costs are significantly reduced, demonstrating the proposed approach's effectiveness and applicability. This study provides insights for logistics enterprises and transportation departments on effectively allocating and utilizing the transportation resources and optimizing the local logistics network.
机译:本研究的重点是具有时间窗口和混合交付和取货的协作多中心车辆配送问题 (CMVRPTWMDP),它是具有混合交付和取货的车辆路径问题 (VRP) 和具有同时交付和取货和时间窗口的 VRP 的变体。采用协同和交通资源共享的方式,优化CMVRPTWDP车辆路线,将送货和取货服务与时间窗口相结合,构建开闭混合车辆路线。首先,将CMVRPTWMDP表述为混合整数规划模型,以最小化物流运营成本;在模型制定中考虑了运输资源共享对减少所需车辆数量和维护成本的影响。其次,设计了一种结合客户聚类和车辆路径优化的两阶段混合算法来求解CMVRPTWMDP;提出一种基于时空距离和客户需求的改进三维k均值聚类算法,将客户重新分配到物流设施(如配送中心或取货中心)。此外,还设计了一种结合了遗传算法(GA)和粒子群优化(PSO)算法的混合启发式算法,称为GA-PSO,用于优化车辆路线。GA和PSO之间的协调算子旨在允许粒子和染色体相互作用,增加粒子群的多样性和找到可行解决方案的可能性。然后,通过与使用30个小规模实例的CPLEX求解器和其他使用25个基准实例的现有算法进行比较,测试了所提方法的性能和有效性。第四,采用最小成本节约(MCRS)模型,为协作联盟参与者设计公平合理的利润分配方案,维护联盟稳定。最后,通过对中国重庆市真实案例研究的优化结果,表明运输资源误用和物流运营成本显著降低,证明了所提方法的有效性和适用性。本研究为物流企业和运输部门有效配置和利用运输资源,优化当地物流网络提供了启示。

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