A systematic review of multi-depot vehicle routing problems

Amina Nura, Shamsu Abdullahi
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引用次数: 1

Abstract

Multi-Depot Vehicle Routing Problem (MDVRP) is a heuristic optimization problem that capture interest of several researchers' for its applicability to real-life situations. The variant of MDVRP are solved with some certain constraints such as service time, time window, vehicle capacity, travelled distance etc. these makes MDVRP to cover several situations In this, 76 studies related to MDVRP from 2012 to 2022 were systematically reviewed. The studies are review based on their constraints and an application through various fields. The goal of this research is to examine the contemporary state of MDVRP and its applications. To achieve this goal, we formed a comprehensive search process which was employed on high rated scientific journals databases. The search process resulted to numerous important research papers in the research domain which were theoretically reviewed. The research papers found are screened based on the titled, abstract, year of publication and exhaustive reading of full text in order to extract the related information that will address the aim of this study. Finally, the selected studies were categorized based on constraints and real-life applications they tackled. The outcome of our study shows that minimizing travelled distance and service time were the most constraints addressed by the selected studies, transportation network, waste management; distribution problems were the most widely used real-life applications of MDVRP concentrated on. 
多车场车辆路线问题的系统回顾
多车场车辆路径问题(MDVRP)是一个启发式优化问题,由于其在现实生活中的适用性而引起了许多研究者的兴趣。MDVRP的变体解决了一定的约束条件,如服务时间、时间窗、车辆容量、行驶距离等,使MDVRP涵盖了多种情况。本文系统地回顾了2012年至2022年76项与MDVRP相关的研究。综述了这些研究的局限性及其在各个领域的应用。本研究的目的是研究MDVRP的当代状态及其应用。为了实现这一目标,我们在高评价的科学期刊数据库中形成了一个综合的检索过程。搜索过程中产生了许多研究领域的重要研究论文,并对其进行了理论综述。所发现的研究论文根据标题,摘要,出版年份和全文的详尽阅读进行筛选,以提取相关信息,以解决本研究的目的。最后,根据约束条件和他们处理的实际应用对选定的研究进行分类。我们的研究结果表明,最大限度地减少旅行距离和服务时间是所选研究解决的最大限制,交通网络,废物管理;分布问题是MDVRP在现实生活中应用最广泛的问题。
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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