Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem

Kratica, Jozef; Stanimirović, Zorica; Tošić, Dejan V.; Filipović, Vladimir

doi:10.1016/j.ejor.2006.06.056

Cited by 103 publications

(55 citation statements)

References 26 publications

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“…Topcuoglu et al 27 adopted genetic algorithm to determine the number of hubs, location, and the allocation of nonhub nodes. Kratica et al 28 applied genetic algorithm to solve the uncapacitated hub median location problem. However, the genetic algorithm can easily fall into the local optimal solution, the simple genetic algorithm cannot successfully solve many complex problems, and allocation is not necessarily the optimal solution.…”

Section: Solution Methodsmentioning

confidence: 99%

Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport

Zhong

Juan

Zong

et al. 2018

International Journal of Distributed Sensor Networks

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Integration of urban and rural infrastructure is critical to integrating urban and rural public transport. A public transport hub is an important element of infrastructure, and it is the key facilities that serve as transferring points between cities and towns. The location of hub is related to the convenience of travel for urban and rural residents and the closeness of economic interactions between urban and rural areas. In this article, considering the background of the integration of urban and rural public transport, from the perspective of public transport hubs in urban and central town, a multi-level hub-and-spoke network is designed, and the location of integration of urban and rural public transport hub is determined. Based on the connection associated with central towns and the capacity constraints of hubs and to achieve the minimum total cost, this article proposes a mixed-integer programming model that employs a genetic and tabu search hybrid optimization algorithm to validate and analyze, which used the urban and rural public transport data from a specified area of Shandong province in China. The results indicate that the model can simultaneously determine locations for hubs in cities and central towns while minimizing total cost. The hub capacity constraint significantly influences the location of two-level hubs. The hub capacity constraint in the model can reduce the transportation cost for an entire network and optimize the transportation network. This study on urban and rural public transport hub location in a huband-spoke network not only reduces the transportation cost of the network but also completes and supplements the location theory of integration of urban and rural public transport.

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Section: Solution Methodsmentioning

confidence: 99%

Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport

Zhong

Juan

Zong

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

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“…He proposed two approaches to determine the upper bound for the number of hubs, as well as hybrid heuristic (based on the simulated annealing method, tabu list, and improvement procedures) to resolve the uncapacitated single allocation hub location problem. Kratica et al (2007) proposed two genetic algorithm (GA) approaches. The numerical experiments were carried out on the standard OR LIB hub data set.…”

Section: Related Workmentioning

confidence: 99%

Locating Hubs in Transport Networks: An Artificial Intelligence Approach

Teodorović¹,

Šelmić²,

Vukićević³

2014

IJTTE

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Hub facilities serve as switching and transshipment points in transportation and communication networks as well as in logistic systems. Hub networks have an influence on flows on the hub-to-hub links and ensure benefit from economies of scale in inter-hub transportation. The key factors for designing a successful hub-and-spoke network are to determine the optimal number of hubs, to properly locate hubs, and to allocate the non-hubs to the hubs. This paper presents the model to determine the locations of the p-hub facilities in the network and to allocate the non-hubs to the hubs. The problem is solved by the Bee Colony Optimization (BCO) algorithm, and the results are compared with the optimal solutions obtained by CPLEX. The BCO algorithm belongs to the class of stochastic swarm optimization methods. The proposed algorithm is inspired by the foraging habits of bees in the nature. The BCO algorithm was able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are acceptable.

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“…The method has a potential for solving large problem instances. Based on their research, [28] proposed two genetic algorithms for solving an uncapacitated single allocation -hub median problem. They compared their methods with a tabu search, simulated annealing, and path relinking method (PRM) [35].…”

Section: Introductionmentioning

confidence: 99%

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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Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem

Cited by 103 publications

References 26 publications

Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport

Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport

Locating Hubs in Transport Networks: An Artificial Intelligence Approach

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

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