Spatial Analysis of Single Allocation Hub Location Problems

Peker, Meltem; Kara, Bahar Y.; Campbell, James F.; Alumur, Sibel A.

doi:10.1007/s11067-015-9311-9

Cited by 18 publications

(9 citation statements)

References 45 publications

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“…Reference [32] proposed a novel method for analyzing hub location problems by considering clustering-based potential hub sets. They define the travel demand of node as ( + ).…”

Section: Clustering-based Methodmentioning

confidence: 99%

“…To generate the potential hub sets, all nodes need to be sorted by their importance, since the node importance depends on spatial properties and travel demands. The node importance can be measured with different formulations, and one of the best measures identified by [32] is as follows:…”

Section: Clustering-based Methodmentioning

confidence: 99%

“…In addition, there are several other variants of CBS, such as augmented CBS and improved RCBS. The details can be found in [32].…”

Section: Cbsmentioning

confidence: 99%

“…Reference [46] proposed two formulations for capacitated ordered median hub location problems. Reference [32] proposed a new method for single allocation hub location analysis. Based on the spatial and flow properties of nodes, a clustering-based potential hub set (CBS) can be generated, which can help narrow the solution sets and reduce the computational complexity of the problems.…”

Section: Introductionmentioning

confidence: 99%

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Finding p-Hub Median Locations: An Empirical Study on Problems and Solution Techniques

Sun

Dai

Zhang

et al. 2017

Journal of Advanced Transportation

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Hub location problems have been studied by many researchers for almost 30 years, and, accordingly, various solution methods have been proposed. In this paper, we implement and evaluate several widely used methods for solving five standard hub location problems. To assess the scalability and solution qualities of these methods, three well-known datasets are used as case studies: Turkish Postal System, Australia Post, and Civil Aeronautics Board. Classical problems in small networks can be solved efficiently using CPLEX because of their low complexity. Genetic algorithms perform well for solving three types of single allocation problems, since the problem formulations can be neatly encoded with chromosomes of reasonable size. Lagrangian relaxation is the only technique that solves reliable multiple allocation problems in large networks. We believe that our work helps other researchers to get an overview on the best solution techniques for the problems investigated in our study and also stipulates further interest on cross-comparing solution techniques for more expressive problem formulations.

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“…Reference [32] proposed a novel method for analyzing hub location problems by considering clustering-based potential hub sets. They define the travel demand of node as ( + ).…”

Section: Clustering-based Methodmentioning

confidence: 99%

Section: Clustering-based Methodmentioning

confidence: 99%

“…In addition, there are several other variants of CBS, such as augmented CBS and improved RCBS. The details can be found in [32].…”

Section: Cbsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finding p-Hub Median Locations: An Empirical Study on Problems and Solution Techniques

Sun

Dai

Zhang

et al. 2017

Journal of Advanced Transportation

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“…The experimental results showed that the GVNS based heuristics outperformed the bestknown heuristics in terms of solution quality and computational effort. Peker et al (2016) identified general features of optimal hub locations for the USApHMP and then exploited this knowledge to develop a straightforward heuristic methodology for solving the problem. Taner and Kara (2016) proposed a new approach to forecast the change in the demand patterns generated at different locations as a result of the placement of new hubs in the USApHMP.…”

Section: Literature Reviewmentioning

confidence: 99%

Benders Decomposition Algorithms for Two Variants of the Single Allocation Hub Location Problem

Ghaffarinasab

Kara

2018

Netw Spat Econ

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The hub location problem (HLP) is a special type of the facility location problem with numerous applications in the airline industry, postal services, and computer and telecommunications networks. This paper addresses two basic variants of the HLP, namely the uncapacitated single allocation hub location problem (USAHLP) and the uncapacitated single allocation p-hub median problem (USApHMP). Exact solution procedures based on Benders decomposition algorithm are proposed to tackle large sized instances of these problems. The standard Benders decomposition algorithm is enhanced through implementation of several algorithmic refinements such as using a new cut disaggregation scheme, generating strong optimality cuts, and an efficient algorithm to solve the dual subproblems. Furthermore, a modern implementation of the algorithm is used where a single search tree is established for the problem and Benders cuts are successively added within a branch-and-cut framework. Extensive computational experiments are conducted to examine the efficiency of the proposed algorithms. We have been able to solve all the instances of the problems from all three main data sets of the HLP to optimality in reasonable computational times.

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