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Artificial bee colony algorithm with scanning strategy for the periodic vehicle routing problem

机译:带有扫描策略的人工蜂群算法求解周期性车辆路径问题

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The periodic vehicle routing problem (PVRP) is an extension of the vehicle routing problem (VRP). Because it extends the single delivery period to a T-day period (T > 1), PVRP has strong theoretical and practical significance. Since PVRP is an embedded VRP, it is more complex and difficult compared with the general VRP. In this paper, the bee colony algorithm is used to solve the PVRP. To improve the performance of this algorithm, multidimensional heuristic information and a local optimization based on a scanning strategy are used. At the end of this paper, the algorithm is tested by some well-known examples. The results show that the proposed improved bee colony algorithm is a powerful tool for solving the PVRP. It also shows that these two kinds of strategies can significantly improve the performance of the algorithm.
机译:定期车辆路径问题(PVRP)是车辆路径问题(VRP)的扩展。由于将单次交货期延长到T天(T> 1),PVRP具有很强的理论和实践意义。由于PVRP是嵌入式VRP,因此与常规VRP相比,它更加复杂和困难。本文采用蜂群算法求解PVRP。为了提高该算法的性能,使用了多维启发式信息和基于扫描策略的局部优化。在本文的最后,通过一些著名的例子对算法进行了测试。结果表明,所提出的改进的蜂群算法是解决PVRP的有力工具。它还表明,这两种策略可以显着提高算法的性能。

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