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A New Solution Approach To Multi-Depot Vehicle Routing Problem With Ant Colony Optimization

机译:蚁群算法求解多点车辆路径问题的新方法

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The Multi-Depot Vehicle Routing Problem (MDVRP) is an extension of the classical Vehicle Routing Problem (VRP) in which vehicles start from multiple depots to visit customers and return to their depots of origin at the end of their tours. The objective of MDVRP is to minimize the total length of the routes, same as the other types of VRP. In this paper a solution methodology based on Ant Colony Optimization (ACO) inspired by the observation of real ants is proposed to solve MDVRP. ACO includes many algorithms which are many diverse variants of the basic principle. We presented the computational implementations of our model for different benchmark MDVRP problem sets. Besides we also recognized the effects to the solutions of all the parameter changes of Ant Colony Optimization and the effects of angle changes on candidate list in detail.
机译:多站点车辆路径问题(MDVRP)是经典车辆路径问题(VRP)的扩展,其中车辆从多个站点开始拜访客户,并在旅行结束时返回其原始站点。 MDVRP的目标是与其他类型的VRP一样,使路由的总长度最小化。本文提出了一种基于蚁群优化(ACO)的求解方法,该方法受实际蚂蚁观测的启发而提出,用于解决MDVRP。 ACO包含许多算法,这些算法是基本原理的多种变体。我们介绍了针对不同基准MDVRP问题集的模型的计算实现。此外,我们还详细了解了蚁群优化的所有参数更改对解决方案的影响以及候选列表上角度变化的影响。

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