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An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

机译:自适应大邻域搜索启发式算法在集线器中断下解决可靠的多分配集线器位置问题

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摘要

The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.
机译:枢纽位置问题(HLP)是在不同情况下遇到的战略规划问题之一,例如供应链管理,客运和货运行业以及电信。在本文中,我们考虑了在中心中断下的可靠的无失能的多重分配中心位置问题。假定每个开放式集线器设施在使用过程中可能会发生故障,在这种情况下,最初分配给该集线器的客户将被重新分配给其他运营中枢,或者他们没有得到服务,在这种情况下必须支付罚款。该问题被建模为两阶段随机程序,并提出了一种基于自适应大邻域搜索(ALNS)的元启发式算法。进行了基于CAB和TR数据集的大量计算实验。结果表明,所提出的求解方法具有很高的效率。

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