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Volume List  / Volume 4 (3)

Article

LOCATING HUBS IN TRANSPORT NETWORKS: AN ARTIFICIAL INTELLIGENCE APPROACH

DOI: 10.7708/ijtte.2014.4(3).04


4 / 3 / 286-296 Pages

Author(s)

Dušan Teodorović - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Milica Šelmić - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Ivana Vukićević - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -


Abstract

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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Keywords:
hub problem; location theory; Bee Colony Optimization;


Acknowledgements:

This research is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant No. 36002.


References:

Calık, H.; Alumur, S.; Kara, B.; Karasan, O. 2009. A tabu-search based heuristic for the hub covering problem over incomplete hub networks, Computers & Operations Research. DOI: http://dx.doi.org/10.1016/j.cor.2008.11.023, 36(12): 3088-3096.

 

Chen, J.F. 2007. A hybrid heuristic for the uncapacitated single allocation hub location problem, Omega. DOI: http://dx.doi.org/10.1016/j.omega.2005.05.004, 35(2): 211-220.

 

Čupić, A.; Teodorović, D. 2012. A Multi - objective approach to the parcel express service delivery problem, Journal of advanced transportation. DOI: http://dx.doi.org/10.1002/atr.1218, 2042-3195.

 

Davidović, T.; Ramljak, D.; Šelmić, M.; Teodorović, D. 2011. Bee colony optimization for the p-center problem, Computers & Operations Research. DOI: http://dx.doi.org/10.1016/j.cor.2010.12.002, 38(10): 1367-1376.

 

Elhedhli, S.; Wu, H. 2010. A Lagrangean Heuristic for Hub-and-Spoke System Design with Capacity Selection and Congestion, INFORMS Journal on Computing. DOI: http://dx.doi.org/10.1287/ijoc.1090.0335, 22(2): 282-296.

 

Ilić, A.; Urošević, D.; Brimbergc, J.; Mladenović, N. 2010. A general variable neighborhood search for solving the uncapacitated single allocation p-hub median problem, European Journal of Operational Research. DOI: http://dx.doi.org/10.1016/j.ejor.2010.02.022, 206(2): 289-300.

 

Klincewicz, J. 1992. Avoiding local optima in the p-hub location problem using tabu search and grasp, Annals of Operations Research. DOI: http://dx.doi.org/10.1007/BF02060483, 40(1): 283-302.

 

Kratica, J.; Milanović, M.; Stanimirović, Z.; Tošić, D. 2011. An evolutionary-based approach for solving a capacitated hub location problem, Applied Soft Computing. DOI: http://dx.doi.org/10.1016/j.asoc.2010.05.035, 11(2): 1858-1866.

 

Kratica, J.; Stanimirović, Z.; Tošić, D.; Filipović, V. 2007. Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem, European Journal of Operational Research. DOI: http://dx.doi.org/10.1016/j.ejor.2006.06.056, 182(1): 15-28.

 

Lučić, P.; Teodorović, D. 2001. Bee System: Modeling Combinatorial Opt imizat ion Transportation Engineering Problems by Swarm Intelligence. Preprints of the TRISTAN IV Triennial Symposium on Transportation Analysis, Sao Miguel, Azores Islands, Portugal. 441-445.

 

Lučić, P.; Teodorović, D. 2003. Computing with Bees: Attacking Complex Transportation Engineering Problems, International Journal on Artificial Intelligence Tools. DOI: http://dx.doi.org/10.1142/S0218213003001289, 12(3): 375-394.

 

Nikolić, M.; Teodorović, D. 2013. Transit network design by Bee Colony Optimization, Expert Systems with Applications. DOI: http://dx.doi.org/10.1016/j.eswa.2013.05.002, 40(15): 5945-5955.

 

O’Kelly, M.E. 1987. A quadratic integer program for the location of interacting hub facilities, European Journal of Operational Research. DOI: http://dx.doi.org/10.1016/S0377-2217(87)80007-3, 32(3): 393-404.

 

Pérez Pérez, M.; Rodríguez, F.; Moreno-Vega, M. 2007. A hybrid VNS–path relinking for the p-hub median problem, IMA J Management Math. DOI: http://dx.doi.org/10.1093/imaman/dpm013, 18(2): 157-171.

 

Randall, M. 2008. Solution approaches for the capacitated single allocation hub location problem using ant colony optimization, Computational Optimization and Applications. DOI: http://dx.doi.org/10.1007/s10589-007-9069-1, 39(2): 239-261.

 

Teodorović, D. 2007. Transportne mreže (Transportation networks). Univerzitet u Beogradu, Saobraćajni fakultet, Beograd (in Serbian) 428 p.


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