Capacitated hub location routing problem with time windows and stochastic demands for the design of intra-city express systems

作     者：Wu, Yuehui Fang, Hui Qureshi, Ali Gul Yamada, Tadashi 

作者机构：College of Transportation Engineering Dalian Maritime University Liaoning Dalian116026 China Department of Urban Management of Engineering Kyoto University Kyoto 615-8246 Japan of Management Kyoto University Yoshida Honmachi Sakyo-ku Kyoto Kyoto-shi606-8501 Japan 

出 版 物：《European Journal of Operational Research》 (Eur J Oper Res)

年 卷 期：2025年第326卷第2期

页      面：255-269页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was partially supported by the National Natural Science Foundation of China (Grant No 72301052 )  the Japan Society for the Promotion of Science (JSPS)  Kakenhi (Grants-in-Aid for ScientificResearch - C) [ 20K04739 ]  and the Fundamental Research Funds for the Central Universities (Grant No 3132024158 ) 

主　　题：Stochastic programming 

摘      要：This work focuses on planning an intra-city express system in a practical environment. Various operation characteristics, such as vehicle capacity, hub capacity, time windows, and stochastic demands, have been considered. Therefore, we introduce a capacitated hub location routing problem with time windows and stochastic demand and formulate it using a multi-stage recourse model. In this model, long-term decisions (hub location and client-to-hub allocation) are made first, and short-term decisions (vehicle routing) are determined after revealing stochastic variables. To solve the problem, we propose a hybrid stochastic variable neighbourhood search (HSVNS) algorithm, which integrates an adaptive large neighbourhood search (ALNS) algorithm within a stochastic variable neighbourhood search (SVNS) framework. Numerical experiments and case studies indicate that the HSVNS algorithm can provide high-quality solutions within a reasonable computation time for instances with up to 70 clients and that considering stochastic factors can efficiently reduce operation costs, especially for instances with tight vehicle capacity and loose time windows. © 2025 Elsevier B.V.