2000
DOI: 10.1016/s0377-2217(99)00274-x
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On the single-assignment p-hub center problem

Abstract: We study the computational aspects of the single-assignment p-hub center problem on the basis of a basic model and a new model. The new model's performance is substantially better in CPU time than dierent linearizations of the basic model. We also prove the NP-Hardness of the problem. Ó 2000 Elsevier Science B.V. All rights reserved.Keywords: Hub location; Modeling; Complexity Hub location problems arise when it is desirable to consolidate and disseminate¯ows at certain centralized locations called hubs. Typic… Show more

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Cited by 131 publications
(67 citation statements)
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“…Then, Campbell [13] presented a first linearization of O'Kelly [12] model. After that, the majority papers in this scope focus on linearization of O'Kelly's model [12] and solving procedure to minimize total transportation cost (e.g., [14]- [18]). Many different types of HLP were introduced by Campbell [14], such as phub median, p-hub center and p-hub covering.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…Then, Campbell [13] presented a first linearization of O'Kelly [12] model. After that, the majority papers in this scope focus on linearization of O'Kelly's model [12] and solving procedure to minimize total transportation cost (e.g., [14]- [18]). Many different types of HLP were introduced by Campbell [14], such as phub median, p-hub center and p-hub covering.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The emanated flows between non-hub nodes and hub nodes, and between hub nodes are calculated by (18) and (19), respectively:…”
Section: International Journal Of Machine Learning and Computing Volmentioning
confidence: 99%
“…Due to the fact that the p-hub median location problem is NP hard [24], solving RM1, which is a mixed integer nonlinear program, is more difficult, especially in large-scale problems. To overcome this issue, the relaxed mixed integer nonlinear programming, called RM2, is presented to determine risk aversion coefficient.…”
Section: Determining Risk Aversion Coefficientmentioning
confidence: 99%
“…Hamacher et al (2004), Labbé and Yaman (2004), Labbé et al (2005), and Marin et al (2006) Canovas et al (2007), Cunha and Silva (2007), Chen (2007), and Silva and Cunha (2009). Kara and Tansel (2000) and Ernst et al (2009) give integer programming formulations for the p-hub center problem. Pamuk and Sepil (2001) and Meyer et al (2009) propose heuristic algorithms, and Baumgartner (2003) derives valid inequalities and gives a branch-and-cut algorithm.…”
Section: Literature Reviewmentioning
confidence: 99%