A multi-objective hub covering location problem under congestion using simulated annealing algorithm

Geramianfar, R.; Pakzad, M.; Golhashem, H.; Tavakkoli-Moghaddam, R.

Growing Science » Uncertain Supply Chain Management » A multi-objective hub covering location problem under congestion using simulated annealing algorithm

Journals

USCM Volumes

Volume 1 (22)
- Issue 1 (4)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (6)

Volume 2 (32)
- Issue 1 (7)
- Issue 2 (5)
- Issue 3 (10)
- Issue 4 (10)

Volume 3 (39)
- Issue 1 (9)
- Issue 2 (13)
- Issue 3 (10)
- Issue 4 (7)

Volume 4 (31)
- Issue 1 (10)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (9)

Volume 5 (26)
- Issue 1 (6)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 6 (25)
- Issue 1 (7)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (6)

Volume 7 (57)
- Issue 1 (8)
- Issue 2 (19)
- Issue 3 (14)
- Issue 4 (16)

Volume 8 (82)
- Issue 1 (20)
- Issue 2 (15)
- Issue 3 (17)
- Issue 4 (30)

Volume 9 (117)
- Issue 1 (25)
- Issue 2 (26)
- Issue 3 (32)
- Issue 4 (34)

Volume 10 (150)
- Issue 1 (28)
- Issue 2 (32)
- Issue 3 (44)
- Issue 4 (46)

Volume 11 (190)
- Issue 1 (42)
- Issue 2 (45)
- Issue 3 (50)
- Issue 4 (53)

Volume 12 (244)
- Issue 1 (55)
- Issue 2 (59)
- Issue 3 (63)
- Issue 4 (67)

Volume 13 (45)
- Issue 1 (15)
- Issue 2 (15)
- Issue 3 (15)

Countries

Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print) Quarterly Publication Volume 1 Issue 3 pp. 153-164 , 2013

A multi-objective hub covering location problem under congestion using simulated annealing algorithm Pages 153-164 Download PDF

Authors: Ramin Geramianfar, Mohammadreza Pakzad, Hamidreza Golhashem, Reza Tavakkoli-Moghaddam

Keywords: Hub location, Multi objective decision making, Simulated annealing

Abstract: Hub covering problem is one of the most popular areas of research due to wide ranges of applications in different service or manufacturing industries. This paper considers a multi-objective hub covering location problem under congestion. The proposed study of this paper considers two objectives where the first one minimizes total transportation cost and the second one minimizes total waiting time for all hobs. The resulted multi-objective decision making problem is formulated as mixed integer programming. Simulated annealing is used to solve the resulted model and the performance of the proposed model is compared against two other alternative methods, particle sward optimization and NSGA-II. The results are compared in terms of four criteria including quality metric, mean ideal distance, diversification metric and spacing metric. The results indicate that the proposed model could perform better than the other two alternative methods in terms of quality metric but the results are somehow mix in terms of other three criteria.

How to cite this paper

 Geramianfar, R., Pakzad, M., Golhashem, H & Tavakkoli-Moghaddam, R. (2013). A multi-objective hub covering location problem under congestion using simulated annealing algorithm.Uncertain Supply Chain Management, 1(3), 153-164.

Refrences

Abdinnour-Helm, S. (2001). Using simulated annealing to solve the p-hub median problem. International Journal of Physical Distribution & Logistics Management, 31(3), 203-220.

Alumur, S., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European Journal of Operational Research, 190(1), 1-21.

Aykin, T. (1995). Networking policies for hub-and-spoke systems with application to the air transportation system. Transportation Science, 29(3), 201-221.

Boland, N., Krishnamoorthy, M., Ernst, A. T., & Ebery, J. (2004). Preprocessing and cutting for multiple allocation hub location problems. European Journal of Operational Research, 155(3), 638-653.

Brooks, S. P., & Morgan, B. J. T. (1995). Optimization using simulated annealing. The Statistician, 241-257.

de Camargo, R. S., Miranda, G., & Luna, H. P. (2008). Benders decomposition for the uncapacitated multiple allocation hub location problem. Computers & Operations Research, 35(4), 1047-1064.

Contreras, I., D?az, J. A., & Fern?ndez, E. (2009). Lagrangean relaxation for the capacitated hub location problem with single assignment. OR spectrum, 31(3), 483-505.

Ebery, J., Krishnamoorthy, M., Ernst, A., & Boland, N. (2000). The capacitated multiple allocation hub location problem: Formulations and algorithms. European Journal of Operational Research, 120(3), 614-631.

Ernst, A. T., & Krishnamoorthy, M. (1999). Solution algorithms for the capacitated single allocation hub location problem. Annals of Operations Research, 86, 141-159.

Karimi, P., Shadlou, S., & Nazari, B. (2012). Introduction of a novel and powerful optimization method using genetic algorithm and finite element method. Advanced Materials Research, 433, 746-753.

Labbé, M., Yaman, H., & Gourdin, E. (2005). A branch and cut algorithm for hub location problems with single assignment. Mathematical programming, 102(2), 371-405.

Mar?n, A. (2005). Formulating and solving splittable capacitated multiple allocation hub location problems. Computers & Operations Research, 32(12), 3093-3109.

Pirkul, H., & Schilling, D. A. (1998). An efficient procedure for designing single allocation hub and spoke systems. Management Science, 44(12-Part-2), S235-S242.
Sasaki, M., & Fukushima, M. (2003). On the hub-and-spoke model with arc capacity constraints. Journal of the Operations Research Society of Japan-Keiei Kagaku, 46(4), 409-428.