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A Hierarchical Bio-inspired Computing for the Hub Location-Routing Problem in Parcel Service

机译:包裹服务中枢纽位置路由问题的层次生物启发式计算

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This paper deals with a hub location-routing problem (HLRP) which integrates the hub location problem (HLP) and the multi-hub vehicle routing problem (MH-VRP) in parcel delivery service. The HLRP is very difficult to be solved since it is composed of two NP-hard problems, the HLP and the MH-VRP. To solve these problems simultaneously we present a novel solution framework which divides the problem into two sub-problems: HLP in the first stage and the MH-VRP in the second one. The two-phase approach hybridizes two bio-inspired computing techniques, an ant colony optimization algorithm for the first stage with a genetic algorithm for the second stage, respectively. We determine the locations of prescribed p hubs within a set of candidate potential parcel sorting centers (PSC), the allocation of each customer node to each selected PSC as well as the vehicle routes for each PSC in order to minimize the sum of the fixed hub costs, traveling costs and fixed vehicle costs. A hierarchical bio-inspired computing based on an ant colony optimization and a genetic search is developed which solves the HLP and VRP hierarchically. The performance of the proposed solution method is investigated through a computational experiment.
机译:本文讨论了枢纽位置路由问题(HLRP),该问题在包裹交付服务中将枢纽位置问题(HLP)和多枢纽车辆路由问题(MH-VRP)集成在一起。 HLRP由HNP和MH-VRP这两个NP难题组成,因此很难解决。为了同时解决这些问题,我们提出了一个新颖的解决方案框架,将该问题分为两个子问题:第一阶段为HLP,第二阶段为MH-VRP。两阶段方法将两种生物启发的计算技术进行了混合,分别是第一阶段的蚁群优化算法和第二阶段的遗传算法。我们确定一组潜在的潜在包裹分类中心(PSC)中指定的p个枢纽的位置,每个客户节点对每个选定PSC的分配以及每个PSC的车辆路线,以最大程度地减少固定枢纽的总和费用,旅行费用和固定车辆费用。开发了一种基于蚁群优化和遗传搜索的分层生物启发式计算方法,该算法可分层解决HLP和VRP。通过计算实验研究了所提出的解决方法的性能。

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