基于路径重新定位和节省启发式的多车场车辆路径分配路径分割

F. Morsidi
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引用次数: 1

摘要

本文采用路线分段优化方法对城市仓库与客户之间的最优配电网进行规划。在本实验中,提出了三个步骤:利用局部搜索属性搜索初始解,利用路径重新定位改进解,利用结合节省启发式的禁忌搜索扰动解。通过应用多车辆段同时部署的理想调度策略和保证成本最优的路由启发式方法,提出了一种增强调度系统优化的替代方案。基于重新填充和顺序插入算法创建初始解,路径重定位和禁忌交换机制构成改进策略和扰动。测试结果表明,将提出的解决方案与先前的遗传算法解决方案进行比较,可以更好地安排代表客户集群的路由隔离方面。事实证明,该策略在优化路径隔离方面比原遗传算法更成功。这表明在路由优化方面有了显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distribution Path Segmentation Using Route Relocation and Savings Heuristics for Multi-Depot Vehicle Routing
This paper uses routing segmentation optimization for the planning of optimal distribution networks between urban depots and their respective customers. In this experiment, three steps are proposed in concession: search for the initial solution using local search properties, improve the solution using route relocation and perturb the solution using tabu search incorporating the savings heuristic. By applying multi-depot simultaneous deployment with ideal scheduling strategies and routing heuristics ensuring cost-optimal routing, the study presents an alternative to enhanced scheduling system optimization. Based on repopulation and sequential insertion algorithms, the initial solution is created, while route relocation and tabu swap mechanisms constitute the improvement strategy and perturbation. Test results comparing the proposed solution strategy to the previous genetic algorithm solution result in a better arrangement of route segregation aspects representing customer clusters. This strategy has proven to be more successful in optimizing route segregation than the original genetic algorithm solution. This demonstrates a significant improvement in route optimization.
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