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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名解决车辆路由问题

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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,我们涉及一个真实世界的车辆路由问题(VRP),显示VRP的特点,其中Windows,多个仓库和站点依赖项。传输请求数据的分析表明,问题对时间限制而过度约束,即在客户网站提供的最大路由持续时间和时间窗口。我们的研究结果表明,蚁群优化与随机排名相结合,提供了处理过度约束问题的适当手段。我们调查的重要意义是在解决解决方案建设中的特定问题启发式的发展。计算结果表明,精致距离启发式的组合,考虑到在仓库执行拾取操作时的客户网站之间的距离,以及在执行拾取操作时估算违反最大路由持续时间和交付时间窗口的启发式,为正在考虑的VRP提供最佳结果。

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