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

IF 6 2区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Yuehui Wu, Hui Fang, Ali Gul Qureshi, Tadashi Yamada
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引用次数: 0

Abstract

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.
具有时间窗和随机需求的城市快速路枢纽选址问题
本工作的重点是在实际环境中规划城际快速系统。考虑了车辆容量、轮毂容量、时间窗和随机需求等各种运行特性。因此,我们引入了一个带时间窗和随机需求的有容量集线器位置路由问题,并采用多阶段追索模型进行了求解。在该模型中,首先进行长期决策(枢纽位置和客户到枢纽分配),在揭示随机变量后确定短期决策(车辆路线)。为了解决这一问题,我们提出了一种混合随机变量邻域搜索(HSVNS)算法,该算法将自适应大邻域搜索(ALNS)算法集成在随机变量邻域搜索(SVNS)框架中。数值实验和算例研究表明,HSVNS算法能够在合理的计算时间内为最多70个客户端的实例提供高质量的解,并且考虑随机因素可以有效地降低运行成本,特别是在车辆容量紧张和时间窗松散的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Journal of Operational Research
European Journal of Operational Research 管理科学-运筹学与管理科学
CiteScore
11.90
自引率
9.40%
发文量
786
审稿时长
8.2 months
期刊介绍: The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.
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