ACO and CP Working Together to Build a Flexible Tool for the VRP

N. ZakeriNejad, D. Riera, Daniel Guimarans
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引用次数: 1

Abstract

In this paper a flexible hybrid methodology, combining Ant Colony Optimisation (ACO) and Constraint Programming (CP), is presented for solving Vehicle Routing Problems (VRP). The stress of this methodology is on the word ‘flexible’: It gives reasonably good results to changing problems without high solution redesign efforts. Thus a different problem with a new set of constraints and objectives requires no changes to the search algorithm. The search part (driven by ACO) and the model of the problem (included in the CP part) are separated to take advantage of their best attributes. This separation makes the application of the framework to different problems much simpler. To assess the feasibility of our approach, we have used it to solve different instances of the VRP family. These instances are built by combining different sets of constraints. The results obtained are promising but show that the methodology needs deeper communication between ACO and CP to improve its performance.
ACO和CP共同为VRP构建灵活的工具
本文将蚁群优化算法与约束规划算法相结合,提出了一种求解车辆路径问题的灵活混合方法。这种方法的重点在于“灵活”这个词:它在不需要大量重新设计解决方案的情况下,为不断变化的问题提供了相当好的结果。因此,一个带有一组新的约束和目标的不同问题不需要改变搜索算法。将搜索部分(由蚁群算法驱动)和问题模型(包含在CP部分)分离,以利用它们的最佳属性。这种分离使得框架在不同问题上的应用变得简单得多。为了评估我们的方法的可行性,我们用它来解决VRP家族的不同实例。这些实例是通过组合不同的约束集来构建的。得到的结果是有希望的,但表明该方法需要在蚁群算法和蚁群算法之间进行更深入的沟通以提高其性能。
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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