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Efficient Benders decomposition algorithms for the robust multiple allocation incomplete hub location problem with service time requirements

机译:有效的Benders分解算法,用于具有服务时间要求的健壮的多分配不完整枢纽定位问题

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Many transportation systems for routing flows between several origin-destination pairs of demand nodes have been widely designed as hub-and-spoke networks. To improve the provided service level of these networks, service time requirements are here considered during modeling, giving rise to a multiple allocation incomplete hub location problem with service time requirements. The problem consists of designing a hub and spoke network by locating hubs, establishing inter-hub arcs, and routing origin-destination demand flows at minimal cost while meeting some service time requirements. As travel times are usually uncertain for most real cases, the problem is approached via a binary linear programming robust optimization model, which is solved by two specialized Benders decomposition algorithms. The devised Benders decomposition framework outperforms a general purpose optimization solver on solving benchmark instances of the hub location literature. The achieved results also show how the probability of violating the travel time requirements decreases with the prescribed protection level, at the expense of the higher costs of the optimal solution for the robust optimization model. (C) 2017 Elsevier Ltd. All rights reserved.
机译:许多用于在多个需求节点的始发-目的地对之间路由流的运输系统已被广泛设计为中心辐射型网络。为了提高这些网络提供的服务水平,在建模过程中考虑了服务时间要求,这导致了具有服务时间要求的多重分配不完整的集线器位置问题。问题包括通过定位集线器来设计集线器和分支网络,建立集线器间弧,并以最低的成本路由起点-目的地需求流,同时满足某些服务时间要求。由于对于大多数实际情况而言,旅行时间通常是不确定的,因此该问题通过二进制线性规划鲁棒优化模型来解决,该模型由两种专门的Benders分解算法解决。在解决枢纽位置文献的基准实例时,设计的Benders分解框架优于通用优化解决方案。所获得的结果还表明,违反行驶时间要求的可能性如何随着规定的保护级别而降低,但以健壮的优化模型的最佳解决方案的较高成本为代价。 (C)2017 Elsevier Ltd.保留所有权利。

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