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An Adaptive Variable Neighborhood Search for a Heterogeneous Fleet Vehicle Routing Problem with Three-Dimensional Loading Constraints

机译:具有三维负荷约束的异构机群车辆路径问题的自适应变量邻域搜索

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The paper addresses the heterogeneous fleet vehicle routing problem with three-dimensional (3D) loading constraints (3L-HFVRP), a new practical variant of the combined routing and loading problem. In this problem, the loads consist of a set of three-dimensional, rectangular shaped items. The fleet is composed of heterogeneous vehicles with different weight and space capacities. The objective is to serve all customers by selecting a set of vehicles such that the total transportation cost is minimized. The cost consists of the fixed cost of the selected vehicles and their travel cost. In addition, loading sequence related constraints frequently encountered in realistic applications are respected when loading and unloading the items. To solve this challenging problem, we develop an adaptive variable neighborhood search (AVNS) which utilizes an extreme point based first fit heuristic to find a feasible loading pattern for each route. We design two strategies to accelerate the loading and routing processes. The Trie data structure is used to record the loading information of routes already visited and to control the computational effort spent for each route. The Fibonacci heap data structure is used to maintain all of the possible moves and vehicle type assignments, which avoids the duplicated evaluation of some moves and unnecessary loading check of unpromising solutions. The robustness and effectiveness of the proposed algorithm is validated by computational tests performed both on some newly generated 3L-HFVRP instances and well-known benchmark instances from the literature for two simplified VRP variants: the capacitated vehicle routing problem with 3D loading constraints (3L-CVRP) and the pure heterogeneous fleet vehicle routing problem (HFVRP). The numerical experiments show that the proposed AVNS outperforms other algorithms in 3L-CVRP and improves several best known solutions reported in the literature. The results obtained for the pure HFVRP are very close to t- e best known solutions.
机译:本文解决了具有三维(3D)装载约束(3L-HFVRP)的异构机队车辆路线问题,这是组合路线和装载问题的新实用变体。在此问题中,负载由一组三维矩形物品组成。车队由重量和空间容量不同的异构车辆组成。目的是通过选择一组车辆以使总运输成本最小化来为所有客户提供服务。成本包括所选车辆的固定成本及其旅行成本。另外,在装载和卸载物品时,还要考虑实际应用中经常遇到的与装载顺序相关的约束。为了解决这一具有挑战性的问题,我们开发了一种自适应变量邻域搜索(AVNS),该算法利用基于极限点的首次拟合启发式算法为每条路线找到可行的加载方式。我们设计了两种策略来加速加载和路由过程。 Trie数据结构用于记录已经访问过的路线的负载信息,并控制每个路线花费的计算量。 Fibonacci堆数据结构用于维护所有可能的移动和车辆类型分配,从而避免了对某些移动的重复评估以及对毫无希望的解决方案的不必要的负载检查。该算法的鲁棒性和有效性通过对一些新生成的3L-HFVRP实例和文献中针对两个简化VRP变体的知名基准实例执行的计算测试进行了验证:具有3D负载约束(3L- CVRP)和纯异构车队车辆选路问题(HFVRP)。数值实验表明,提出的AVNS在3L-CVRP中优于其他算法,并改进了文献中报道的几种最著名的解决方案。纯HFVRP获得的结果非常接近最著名的解决方案。

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