A branch‐and‐cut algorithm for hub network design problems with profits

Yuan Gao, Jun Xia, Hua Ke
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Abstract

Hub network design problems with profits (HNDPPs) extend the classical hub location problems (HLPs), with the profit maximization objective. HNDPPs aim to design a service network with hub nodes and hub links, select the commodities to be served, and determine the transportation routes for the selected commodities. In this article, we allow the transportation route of each commodity to contain more than one hub link, which generalizes existing HNDPPs and is known to be more profitable. A new path‐based formulation is proposed, which is then reformulated in a Benders decomposition fashion, and an exact branch‐and‐cut algorithm is developed to solve the Benders master problem. At the nodes of the enumeration tree, Benders decomposition algorithm is used to solve a linear relaxation of the Benders master problem. When implementing the Benders decomposition algorithm, the special structure of the subproblem is explored, based on which an efficient method is proposed to generate Benders cuts. In addition, refinements and a heuristic strategy are used to speed up the branch‐and‐cut algorithm. Extensive numerical experiments on benchmark instances have been carried out to verify the effectiveness and efficiency of the proposed solution method.
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具有利润的集线器网络设计问题的分支切断算法
带利润的枢纽网络设计问题(HNDPPs)是经典枢纽选址问题的扩展,以利润最大化为目标。hndpp旨在设计具有枢纽节点和枢纽链路的服务网络,选择要服务的商品,并确定所选商品的运输路线。在本文中,我们允许每种商品的运输路线包含多个枢纽链接,这是对现有hndpp的概括,并且已知更有利可图。提出了一个新的基于路径的公式,然后以Benders分解的方式重新表述,并开发了一个精确的分支和切割算法来解决Benders主问题。在枚举树节点处,采用Benders分解算法求解一个线性松弛的Benders主问题。在实现Benders分解算法时,探讨了子问题的特殊结构,并在此基础上提出了生成Benders切割的有效方法。此外,采用了改进和启发式策略来加快分支切断算法的速度。在基准实例上进行了大量的数值实验,验证了所提出的求解方法的有效性和高效性。
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