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Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

机译:使用资源共享和时间窗口分配来多派回收车辆路由问题

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This study aims to provide tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing (RS) and time window assignment (TWA) strategies. The RS strategy contributes to efficient resource allocation and utilization among recycling centers (RCs). The TWA strategy involves assigning time windows to customers to enhance the operational efficiency of logistics networks. A biobjective mathematical model is established to minimize the total operating cost and number of vehicles for solving the multidepot recycling vehicle routing problem with RS and TWA (MRVRPRSTWA). A hybrid heuristic algorithm including 3D k -means clustering algorithm and nondominated sorting genetic algorithm- (NSGA-) II (NSGA-II) is designed. The 3D k -means clustering algorithm groups customers into clusters on the basis of their spatial and temporal distances to reduce the computational complexity in optimizing the multidepot logistics networks. In comparison with NSGA algorithm, the NSGA-II algorithm incorporates an elitist strategy, which can improve the computational speed and robustness. In this study, the performance of the NSGA-II algorithm is compared with the other two algorithms. Results show that the proposed algorithm is superior in solving MRVRPRSTWA. The proposed model and algorithm are applied to an empirical case study in Chongqing City, China, to test their applicability in real logistics operations. Four different scenarios regarding whether the RS and TWA strategies are included or not are developed to test the efficacy of the proposed methods. The results indicate that the RS and TWA strategies can optimize the recycling services and resource allocation and utilization and enhance the operational efficiency, thus promoting the sustainable development of the logistics industry.
机译:本研究旨在考虑资源共享(RS)和时间窗口分配(TWA)策略提供多派回收物流网络中的战术和操作决策。 RS战略有助于回收中心(RCS)的有效资源分配和利用。 TWA策略涉及为客户分配时间窗口以提高物流网络的运行效率。建立了生物学数学模型,以最大限度地减少求解RS和TWA(MRVRPRSTWA)的多派回收车辆路由问题的总运营成本和车辆数量。设计了一种混合启发式算法,包括3D k -means聚类算法和NondoMinated分类遗传算法 - (NSGA-II)(NSGA-II)。基于它们的空间和时间距离,3D K -Means集群算法将客户组成集群,以降低优化多派物流网络的计算复杂性。与NSGA算法相比,NSGA-II算法包含了一种精英策略,可以提高计算速度和鲁棒性。在这项研究中,将NSGA-II算法的性能与其他两个算法进行了比较。结果表明,该算法在解决MRVRPRSTWA方面优越。拟议的模型和算法适用于中国重庆市的实证研究,以在实际物流业务中测试其适用性。有关是否包括RS和TWA策略的四种不同的情景,以开发出测试所提出的方法的功效。结果表明,RS和TWA策略可以优化回收服务和资源分配和利用,提高运营效率,从而促进物流业的可持续发展。

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