A REVIEW OF SINGLE AND POPULATION-BASED METAHEURISTIC ALGORITHMS SOLVING MULTI DEPOT VEHICLE ROUTING PROBLEM

International Journal of Software Engineering and Computer Systems Pub Date : 2018-08-30 DOI:10.15282/IJSECS.4.2.2018.6.0050

Sherylaidah Samsuddin, M. Othman, L. M. Yusuf

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引用次数: 6

Abstract

Multi-Depot Vehicle Routing Problem (MDVRP) arises with rapid development in the logistics and transportation field in recent years. This field, mainly, faces challenges in arranging their fleet efficiently to distribute the goods to customers by minimizing distance and cost. Therefore, the decision maker needs to specify the vehicles to reach the particular depot which, serves the customers with the predetermined capacity. Hence, to solve the stated problems, there is a need to apply metaheuristic methods to get minimal transportation costs. This article reviews on single and population-based metaheuristic methods solving MDVRP from the year 2013 until 2018. The methods discussed were simulated annealing (SA), variable neighborhood search (VNS), ant colony algorithm (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). From the previous works, it can be concluded that the application of population based metaheuristic gives better solutions in solving MDVRPs. Keywords: Metaheuristic, Multi Depot, MDVRP

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求解多车场车辆路径问题的单一和基于群体的元启发式算法综述

多车场车辆路径问题是近年来随着物流运输领域的迅速发展而产生的问题。该领域主要面临的挑战是如何有效地安排他们的车队，以最大限度地减少距离和成本，将货物配送给客户。因此，决策者需要指定车辆到达特定的仓库，以预定的容量为客户服务。因此，为了解决上述问题，需要应用元启发式方法以获得最小的运输成本。本文回顾了从2013年到2018年求解MDVRP的单个和基于群体的元启发式方法。讨论了模拟退火(SA)、变邻域搜索(VNS)、蚁群算法(ACO)、粒子群优化(PSO)和遗传算法(GA)。从以往的工作中可以看出，应用基于群体的元启发式算法可以更好地解决mdvrp问题。关键词:元启发式，多仓库，MDVRP

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International Journal of Software Engineering and Computer Systems

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