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An improved adaptive large neighborhood search algorithm for the heterogeneous fixed fleet vehicle routing problem

机译：改进的自适应大邻域搜索算法求解异构固定车队车辆路径问题

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摘要

The heterogeneous fixed fleet vehicle routing problem (HFFVRP) is a variant problem of the ordinary capacitated vehicle routing problem (CVRP) when considering the fixed number of different vehicles, with distinct capacities and fixed or variable cost. To address large-scale HFFVRP, an improved adaptive large neighborhood search algorithm (iALNS) is proposed and implemented. According to the clustered features of customers in the application scene, a cluster removal method is incorporated into the original several removal methods of adaptive large neighborhood search algorithm (ALNS). Additionally, we develop a constructive insertion method to improve the traditional greedy insertion method. To ensure that the algorithm can get results within the limited time, an adaptive selection strategy based on time constraint is designed to select removal and repair method. The iALNS is tested in benchmark instances with up to 360 customers. The results obtained are quite competitive with those found in the literature.
机译：当考虑固定数量的不同车辆，具有不同的容量以及固定或可变成本时，异构固定车队车辆路径问题（HFFVRP）是普通容量车辆路径问题（CVRP）的变体问题。为了解决大规模HFFVRP问题，提出并实现了一种改进的自适应大邻域搜索算法（iALNS）。根据应用场景中客户的聚类特征，将聚类去除方法并入了自适应大邻域搜索算法（ALNS）最初的几种去除方法。此外，我们开发了一种建设性的插入方法来改进传统的贪婪插入方法。为了保证算法能够在有限的时间内得到结果，设计了一种基于时间约束的自适应选择策略来选择去除和修复的方法。 iALNS已在多达360个客户的基准实例中进行了测试。获得的结果与文献中的结果相当有竞争力。

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