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Solving a Vehicle Routing Problem with Ant Colony Optimisation and Stochastic Ranking

机译:用蚁群优化和随机排序求解车辆路径问题

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In our contribution we are concerned with a real-world vehicle routing problem (VRP), showing characteristics of VRP with time windows, multiple depots and site dependencies. An analysis of transport request data reveals that the problem is over-constrained with respect to time constraints, i.e. maximum route durations and time windows for delivery at customer sites. Our results show that ant colony optimisation combined with stochastic ranking provides appropriate means to deal with the over-constrained problem. An essential point in our investigations was the development of problem-specific heuristics, guiding ants in the construction of solutions. Computational results show that the combination of a refined distance heuristic, taking into account the distances between customer sites when performing pickup operations at depots, and a look-ahead heuristic, estimating the violation of maximum route durations and delivery time windows when performing pickup operations, provides the best results for the VRP under consideration.
机译:在我们的贡献中,我们关注的是现实世界中的车辆路径问题(VRP),该问题显示了具有时间窗口,多个仓库和站点依存关系的VRP的特征。对运输请求数据的分析表明,该问题在时间限制(即最大路线持续时间和在客户现场交付的时间窗口)方面受到过度限制。我们的结果表明,蚁群优化与随机排序相结合为解决过度约束的问题提供了适当的手段。我们调查的重点是开发特定问题的启发式方法,指导蚂蚁构建解决方案。计算结果表明,结合精致的距离启发法(考虑到在仓库进行取货操作时客户站点之间的距离)和前瞻性启发法(估计出在执行取货操作时违反最大路线持续时间和交货时间窗口),为所考虑的VRP提供最佳结果。

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