Deng Shejun, Yuan Yingying, Wang Yong, Wang Haizhong, Koll Charles
College of Civil Science and Engineering, Yangzhou University, Yangzhou, China.
School of Management, Shanghai University, Shanghai, China.
PLoS One. 2020 Nov 23;15(11):e0242555. doi: 10.1371/journal.pone.0242555. eCollection 2020.
Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.
多中心物流配送网络中物流设施之间的协作可以通过包括物流设施、车辆和客户服务在内的资源共享，显著提高物流资源的利用率。本研究提出并测试了不同的资源共享方案，以解决基于资源共享的协作式多中心物流配送网络（CMCLDN-RS）的优化问题。CMCLDN-RS问题旨在建立一种物流资源分配的协作机制，以提高物流网络的运营效率。考虑到多个服务期内的各种资源共享方案，提出了一个双目标优化模型，以最小化总成本和车辆数量。设计了一种基于客户聚类的自适应网格粒子群优化（AGPSO）算法来解决CMCLDN-RS问题并找到帕累托最优解。为该算法设计了一种有效的精英迭代和选择性赋权机制，将全局搜索和局部搜索相结合以提高搜索能力。CMCLDN-RS的解决方案通过合适的利润分配模型，保证成本节约能够公平地分配给协作参与者。与现有的非支配排序遗传算法-II和多目标进化算法的计算性能相比，AGPSO的计算效率更高。在中国成都的一个实证案例研究表明，所提出的具有资源共享的协作机制可以有效降低总运营成本和车辆数量，从而提高物流网络的运营效率。
