Solving a Multi-Objective Hub Covering Location Problem with Congestion by a New Algorithm Considering a Queue Theory

Abstract

Hub covering problem (HLP) is a very popular areas of research for its wide ranges of applications in different service or manufacturing industries. This paper considers a bi-objective hub covering location problem with congestion. The objectives minimize the total transportation cost and the total waiting time for all hobs, respectively. The resulted multi-objective decision-making problem is formulated as mixed-integer programming (MIP) model. To solve this presented model, multi-objective parallel simulated annealing (MOPSA) is proposed and its performance is compared with two other meta-heuristics; namely, particle sward optimization (PSO) and non-dominated sorted genetic algorithm (NSGA-II). The computational results are compared in terms of four criteria including quality, mean ideal distance, diversification and spacing metrics. The associated results indicate that the presented model can outperform the other two meta-heuristics in terms of the quality metric.

Keywords

References

[1]       Alumar, S., Kara, B.Y., “Network hub location problems: The state of the art”, European Journal of Operational Research, Vol. 190, pp. 1–21, 2008.
[2]       Labbe´, M., Yaman, H., Gourdin, E., “A branch and cut algorithm for the hub location problems with single assignment”, Mathematical Programming, Vol. 102, pp. 371–405, 2005.
[3]       Ebery, J., Krishnamoorthy, M., Ernst, A., Boland, N., “The capacitated multiple allocation hub location problem: Formulations and algorithms”, European Journal of Operational Research, Vol. 120, pp. 614–631, 2000.
[4]       Ernst, A.T., Krishnamoorthy, M., “Solution algorithms for the capacitated single allocation hub location problem”, Annals of Operations Research, Vol. 86, pp. 141–159, 1999.
[5]       Boland, N., Krishnamoorthy, M., Ernst, A.T., Ebery, J., “Preprocessing and cutting for multiple allocation hub location problems”, European Journal of Operational Research, Vol. 155, pp. 638–653, 2004.
[6]       Marin, A., “Formulating and solving splittable capacitated multiple allocation hub location problems”, Operations and Research, Vol. 32, pp. 3093–3109, 2005.
[7]       Sasaki, M., Fukushima, M., “On the hub-and-spoke model with arc capacity constraints”, Journal of the Operations Research Society of Japan , Vol. 46, pp. 409–428, 2003.
[8]       Camargo, R., S., Miranda, G., Luna, H.P., “Benders decomposition for the uncapacitated multiple allocation hub location problem”, Computers and Operations Research, Vol. 35, pp. 1047 – 1064, 2008.
[9]       Contreras, I., Díaz, J., Fernández, E., “Lagrangean relaxation for the capacitated hub location problem with single assignment”, OR Spectrum, Vol. 31, pp. 483–505, 2009.
[10]   Aykin, T., “Networking policies for hub-and-spoke systems with application to the air transportation system”, Transportation Science, Vol. 29, pp. 201–221, 1995.
[11]   Pirkul, H., Schilling, D.A., “An efficient procedure for designing single allocation hub and spoke systems”, Management Science, Vol. 44, pp. 235–242, 1998.
[12]   Abdinnour-Helm, S.,”Using simulated annealing to solve the p-hub median problem”, International Journal of Physical Distribution & Logistics Management, Vol. 31, pp. 203–220, 2001.
Kirkpatrick, S., Gelatt C.D., Vecchi, M.P., “Optimization by simulated annealing”, Science, Vol. 220, pp. 671–680, 1983.

Files

History

  • Receive Date: 26 April 2014
  • Revise Date: 22 May 2014
  • Accept Date: 09 June 2014

Share

How to cite

Statistics