A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Guner, Ali R.; Sevkli, Mehmet

doi:https://doi.org/10.1155/2008/861512

Journal of Artificial Evolution and Applications

On this page

Abstract References Copyright Related Articles

Special Issue

Particle Swarms: The Second Decade

View this Special Issue

Research Article | Open Access

Volume 2008 | Article ID 861512 | https://doi.org/10.1155/2008/861512

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Ali R. Guner¹and Mehmet Sevkli²

Academic Editor: T. Blackwell

Received05 Jul 2007

Revised27 Dec 2007

Accepted12 Mar 2008

Published08 Apr 2008

Abstract

A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

References

D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi, Designing and Managing the Supply Chain, Concepts, Strategies and Case Studies, McGraw-Hill, Boston, Mass, USA, 2000.
D. Ghosh, “Neighborhood search heuristics for the uncapacitated facility location problem,” European Journal of Operational Research, vol. 150, no. 1, pp. 150–162, 2003.
View at: Publisher Site | Google Scholar | MathSciNet
V. L. Beresnev, E. H. Gimady, and V. T. Dementev, Extremal Problems of Standardization, Nauka, Novosibirsk, Russia, 1978.
E. H. Gimady, The Choice of Optimal Scales in One Problem Class of Location or Standartization, Manage Systems, Nauka, Novosibirsk, Russia, 6th edition, 1970.
G. Corneuéjols, M. L. Fisher, and G. L. Nemhauser, “Location of bank accounts to optimize float: an analytic study of exact and approximate algorithms,” Management Science, vol. 23, no. 8, pp. 789–810, 1977.
View at: Google Scholar
B. M. Khumawala, “An efficient branch and bound algorithm for the warehouse location problem,” Management Science, vol. 18, no. 12, pp. B718–B733, 1972.
View at: Google Scholar
J. Krarup and P. M. Pruzan, “The simple plant location problem: survey and synthesis,” European Journal of Operation Research, vol. 12, no. 1, pp. 36–81, 1983.
View at: Publisher Site | Google Scholar
P. B. Mirchandani and R. L. Francis, Eds., Discrete Location Theory, P. B. Mirchandani and R. L. Francis, Eds., Wiley-Interscience, New York, NY, USA, 1990.
A. Klose, “A branch and bound algorithm for an UFLP with a side constraint,” International Transactions in Operational Research, vol. 5, no. 2, pp. 155–168, 1998.
View at: Publisher Site | Google Scholar
T. J. Van Roy, “A cross decomposition algorithm for capacitated facility location,” Operations Research, vol. 34, no. 1, pp. 145–163, 1986.
View at: Google Scholar
J. Barcelo, Å. Hallefjord, E. Fernandez, and K. Jörnsten, “Lagrangean relaxation and constraint generation procedures for capacitated plant location problems with single sourcing,” OR Spektrum, vol. 12, no. 2, pp. 79–88, 1990.
View at: Publisher Site | Google Scholar
D. Erlenkotter, “A dual-based procedure for uncapacitated facility location,” Operations Research, vol. 26, no. 6, pp. 992–1009, 1978.
View at: Google Scholar
M. Köerkel, “On the exact solution of large-scale simple plant location problems,” European Journal of Operational Research, vol. 39, no. 2, pp. 157–173, 1989.
View at: Publisher Site | Google Scholar | MathSciNet
G. Cornuéjols, G. L. Nemhauser, and L. A. Wolsey, “The uncapacitated facility location problem,” in Discrete Location Theory, pp. 119–171, John Wiley & Sons, New York, NY, USA, 1990.
View at: Google Scholar
M. L. Alves and M. T. Almeida, “Simulated annealing algorithm for the simple plant location problem: a computational study,” Revista Investigaćão Operacional, vol. 12, 1992.
View at: Google Scholar
M. E. Aydin and T. C. Fogarty, “A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems,” Journal of Heuristics, vol. 10, no. 3, pp. 269–292, 2004.
View at: Publisher Site | Google Scholar
K. S. Al-Sultan and M. A. Al-Fawzan, “A tabu search approach to the uncapacitated facility location problem,” Annals of Operations Research, vol. 86, pp. 91–103, 1999.
View at: Publisher Site | Google Scholar | MathSciNet
L. Michel and P. Van Hentenryck, “A simple tabu search for warehouse location,” European Journal of Operational Research, vol. 157, no. 3, pp. 576–591, 2004.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
M. Sun, “Solving the uncapacitated facility location problem using tabu search,” Computers & Operations Research, vol. 33, no. 9, pp. 2563–2589, 2006.
View at: Publisher Site | Google Scholar | MathSciNet
J. Kratica, D. Tošic, V. Filipović, and I. Ljubić, “Solving the simple plant location problem by genetic algorithm,” RAIRO Operations Research, vol. 35, no. 1, pp. 127–142, 2001.
View at: Publisher Site | Google Scholar | MathSciNet
J. H. Jaramillo, J. Bhadury, and R. Batta, “On the use of genetic algorithms to solve location problems,” Computers & Operations Research, vol. 29, no. 6, pp. 761–779, 2002.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
M. Gen, Y. Tsujimura, and S. Ishizaki, “Optimal design of a star-LAN using neural networks,” Computers & Industrial Engineering, vol. 31, no. 3-4, pp. 855–859, 1996.
View at: Publisher Site | Google Scholar
S. Vaithyanathan, L. I. Burke, and M. A. Magent, “Massively parallel analog tabu search using neural networks applied to simple plant location problems,” European Journal of Operational Research, vol. 93, no. 2, pp. 317–330, 1996.
View at: Publisher Site | Google Scholar
R. C. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science (MHS '95), pp. 39–43, Nagoya, Japan, October 1995.
View at: Publisher Site | Google Scholar
F. Van den Bergh and A. P. Engelbecht, “Cooperative learning in neural networks using particle swarm optimizers,” South African Computer Journal, vol. 26, pp. 84–90, 2000.
View at: Google Scholar
A. Salman, I. Ahmad, and S. Al-Madani, “Particle swarm optimization for task assignment problem,” Microprocessors and Microsystems, vol. 26, no. 8, pp. 363–371, 2002.
View at: Publisher Site | Google Scholar
A. Allahverdi and F. S. Al-Anzi, “A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application,” Computers & Operations Research, vol. 33, no. 4, pp. 1056–1080, 2006.
View at: Publisher Site | Google Scholar
M. F. Tasgetiren, Y.-C. Liang, M. Sevkli, and G. Gencyilmaz, “A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem,” European Journal of Operational Research, vol. 177, no. 3, pp. 1930–1947, 2007.
View at: Publisher Site | Google Scholar
R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Evolutionary Programming VII, vol. 1447 of Lecture Notes in Computer Science, pp. 611–616, Springer, Berlin, Germany, 1998.
View at: Publisher Site | Google Scholar
J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks (ICNN '95), vol. 4, pp. 1942–1948, Perth, Australia, November-December 1995.
View at: Google Scholar
S. Naka, T. Genji, T. Yura, and Y. Fukuyama, “A hybrid particle swarm optimization for distribution state estimation,” IEEE Transactions on Power Systems, vol. 18, no. 1, pp. 60–68, 2003.
View at: Publisher Site | Google Scholar
H. Yoshida, K. Kawata, Y. Fukuyama, and Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage stability,” in Proceedings of the International Conference on Intelligent System Application to Power System (ISAP '99), pp. 117–121, Rio de Janeiro, Brazil, April 1999.
View at: Google Scholar
M. Sevkli and A. R. Guner, “A continuous particle swarm optimization algorithm for uncapacitated facility location problem,” in Ant Colony Optimization and Swarm Intelligence, vol. 4150 of Lecture Notes in Computer Science, pp. 316–323, Springer, Berlin, Germany, 2006.
View at: Publisher Site | Google Scholar
J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC '97), vol. 5, pp. 4104–4108, Orlando, Fla, USA, October 1997.
View at: Publisher Site | Google Scholar
P.-Y. Yin, “A discrete particle swarm algorithm for optimal polygonal approximation of digital curves,” Journal of Visual Communication and Image Representation, vol. 15, no. 2, pp. 241–260, 2004.
View at: Publisher Site | Google Scholar
C.-J. Liao, C.-T. Tseng, and P. Luarn, “A discrete version of particle swarm optimization for flowshop scheduling problems,” Computers & Operations Research, vol. 34, no. 10, pp. 3099–3111, 2007.
View at: Google Scholar
Q.-K. Pan, M. F. Tasgetiren, and Y.-C. Liang, “A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem,” Computers & Operations Research, vol. 35, no. 9, pp. 2807–2839, 2008.
View at: Publisher Site | Google Scholar
J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence, Morgan Kaufmann, San Francisco, Calif, USA, 2001.
J. E. Beasley, “OR-Library,” 2005, http://people.brunel.ac.uk/~mastjjb/jeb/info.html.
View at: Google Scholar

Copyright

Copyright © 2008 Ali R. Guner and Mehmet Sevkli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

691

Downloads

0

Citations