A solution to the hub center problem via a single-relocation algorithm with tabu search

Pamuk, F. Selcen; Sepil, Canan

doi:10.1080/07408170108936838

Cited by 32 publications

(12 citation statements)

References 24 publications

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“…The first heuristic for the single allocation p-hub center problem is presented in Pamuk and Sepil (2001). They proposed a single-relocation heuristic for generating location-allocation decisions in a reasonable time and they superimposed tabu search on this underlying algorithm so as to decrease the possibility of being trapped by local optima.…”

Section: Capacitated Hub Location Problemmentioning

confidence: 99%

Network hub location problems: The state of the art

Alumur

Kara

2008

European Journal of Operational Research

749

338

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Hubs are special facilities that serve as switching, transshipment and sorting points in many-to-many distribution systems. The hub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in order to route the traffic between origin-destination pairs. In this paper we classify and survey network hub location models. We also include some recent trends on hub location and provide a synthesis of the literature.

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Section: Capacitated Hub Location Problemmentioning

confidence: 99%

Network hub location problems: The state of the art

Alumur

Kara

2008

European Journal of Operational Research

749

338

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“…Even though there are various studies proposing heuristics for hub location problems with transportation cost objectives (for example [15,16,21,22,24,38]) and few for the center problem [23,31,37], to the best our knowledge, there is a single study presenting a heuristic for the hub covering problem. In this study by Calik et al [8], the authors developed a tabu search based heuristic for the incomplete hub covering problem.…”

Section: A Heuristic Algorithmmentioning

confidence: 99%

Multimodal hub location and hub network design

Alumur

Kara

Karaşan

2012

Omega

172

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a b s t r a c tThrough observations from real life hub networks, we introduce the multimodal hub location and hub network design problem. We approach the hub location problem from a network design perspective. In addition to the location and allocation decisions, we also study the decision on how the hub networks with different possible transportation modes must be designed. In this multimodal hub location and hub network design problem, we jointly consider transportation costs and travel times, which are studied separately in most hub location problems presented in the literature. We allow different transportation modes between hubs and different types of service time promises between origindestination pairs while designing the hub network in the multimodal problem. We first propose a linear mixed integer programming model for this problem and then derive variants of the problem that might arise in certain applications. The models are enhanced via a set of effective valid inequalities and an efficient heuristic is developed. Computational analyses are presented on the various instances from the Turkish network and CAB data set.

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“…Pamuk and Sepil (2001) addressed the p-hub center problem. They proposed the first heuristic for the single allocation p-hub center problem as a means of generating location-allocation strategies in a reasonable amount of time, and superimposed tabu search on the underlying algorithm, so as to decrease the possibility of being trapped by local optima.…”

Section: Fig 1 a Single Allocation (Left Hand Side) And Multiple Almentioning

confidence: 99%

Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing

Rabbani¹,

Kazemi²

2015

10.5267/j.ijiec

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In the existing literature, there are a huge number of studies focused on p-hub median problems and inventing heuristic or metaheuristic algorithms for solving them. But such analogous body of literature does not exist for its counterpart problem; p-hub center problem. In fact, since p-hub center has been lately introduced and has a particular objective function, minimizing the maximum cost between origin-destination nodes, there are few studies investigating the problem and the challenges for solving it. In this study, after presenting a complete definition of the uncapacitated multiple allocation p-hub center problem (UMApHCP) two well-known metaheuristic algorithms are proposed to solve the problem for small scale and large scale standard data sets. These two algorithms are one single solution-based algorithm, Simulated Annealing (SA), and one population-based metaheuristic, Genetic Algorithm (GA). Because of the particular nature of the problem, Dijkstra's algorithm has been incorporated in the fitness function calculation part of the proposed methods. The numerical results of running the GA and SA for standard test problems show that for smaller scale test problems, single solution-based SA shows greater performance versus GA but for larger scales of data sets the GA generally yield more desirable solutions.

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A solution to the hub center problem via a single-relocation algorithm with tabu search

Cited by 32 publications

References 24 publications

Network hub location problems: The state of the art

Network hub location problems: The state of the art

Multimodal hub location and hub network design

Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing

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