求解多车场车辆路径问题的元启发式算法

Omar M. Khairy, Omar M. Shehata, E. I. Morgan
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引用次数: 2

摘要

多仓库车辆路线问题是经典车辆路线问题中最重要和最具挑战性的问题之一,其目标是找到车队服务于多个客户的路线,往返于多个仓库。由于解决这类问题的复杂性,采用了元启发式算法。最有价值球员算法是一种用于解决连续优化问题的新技术。本文采用遗传算法和蚁群算法求解多车场车辆路径问题。针对多车场车辆路径问题,提出了一种混合最有价值参与者算法。该算法在10个不同的问题上进行了测试,并与遗传算法和蚁群优化两种知名技术进行了比较。该算法对小问题的求解结果令人满意,但在大多数测试用例中,遗传算法优于其他两种算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meta-heuristic Algorithms for Solving the Multi-Depot Vehicle Routing Problem
Multi-depot Vehicle Routing Problem is one of the most important and challenging variations of the classical Vehicle Routing Problem, where the goal is to find the routes for a fleet of vehicles to serve a number of customers, travelling from and to several depots. Due to the complexity of solving such problems, meta-heuristic algorithms are used. The Most Valuable Player algorithm is a recent technique used to solve continuous optimization problems. This study uses the Genetic Algorithm and the Ant Colony Optimization to solve the Multi-Depot Vehicle Routing Problem. A Hybrid Most Valuable Player algorithm is also proposed to solve the multi-depot vehicle routing problem. The algorithm was tested on 10 different problems and compared to two well-known techniques, Genetic Algorithm and Ant Colony Optimization. Results of the developed algorithm were satisfactory for small sized problems, however Genetic Algorithm surpassed both other algorithms in most test cases.
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