Abstract
p-Median problem is one of the most applicable problem in the areas of supply chain management and operation research. There are various versions of these problems. Continuous p-median is one of them where the facility points and the demand points lie in an ’n’ dimensional hyperspace. It has been proved that this problem is NP-complete and most of the algorithms that have been defined are mere approximations. In this paper, we present a meta-heuristic based approach that calculates the median points given a set of demand points with arbitrary demands. The algorithm is a combination of genetic algorithms, particle swarm optimization and a number of novel techniques that aims to further improve the result. The algorithm is tested on known data sets as and we show’s its performance in comparison to other known algorithms applied on the same problem.
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References
Gudehus, T., Kotzab, H.: Comprehensive logistics. Springer (2009)
Wesolowsky, G.O.: The weber problem: History and perspectives. Computers & Operations Research (1993)
Rosing, K.: Towards the solution of the (generalised) multi-Weber problem. Economisch Geografisch Instituut, Erasmus Universiteit (1990)
Kariv, O., Hakimi, S.L.: An algorithmic approach to network location problems. ii: The p-medians. SIAM Journal on Applied Mathematics 37(3), 539–560 (1979)
Talbi, E.G.: Metaheuristics: from design to implementation, vol. 74. John Wiley & Sons (2009)
Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. Springer (1987)
Goldberg, D.E.: Genetic algorithms. Pearson Education India (2006)
Glover, F., Laguna, M.: Tabu search. Springer (1999)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)
Weiszfeld, E.: Sur le point pour lequel la somme des distances de n points donnés est minimum. Tohoku Math. J. 43(355-386), 2 (1937)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86. IEEE (2001)
Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Natural Computing 1(2-3), 235–306 (2002)
Imran, M., Hashim, R., Khalid, N.E.A.: An overview of particle swarm optimization variants. Procedia Engineering 53, 491–496 (2013)
Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. part ii: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing 7(1), 109–124 (2008)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)
Bongartz, I., Calamai, P.H., Conn, A.R.: A projection method forl p norm location-allocation problems. Mathematical Programming 66(1-3), 283–312 (1994)
Beasley, J.E.: Or-library: distributing test problems by electronic mail. Journal of the Operational Research Society, 1069–1072 (1990)
Aras, N., Özkısacık, K., Altınel, İ.K.: Solving the uncapacitated multi-facility weber problem by vector quantization and self-organizing maps. Journal of the Operational Research Society 57(1), 82–93 (2006)
Gamal, M., Salhi, S.: A cellular heuristic for the multisource weber problem. Computers & Operations Research 30(11), 1609–1624 (2003)
Salhi, S., Gamal, M.: A genetic algorithm based approach for the uncapacitated continuous location–allocation problem. Annals of Operations Research 123(1-4), 203–222 (2003)
Brito, J., Martínez, F.J., Moreno, J.A.: Particle swarm optimization for the continuous p-median problem. In: 6th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, CIMMACS, pp. 14–16. Citeseer (2007)
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Borah, S., Dewan, H. (2014). A Hybrid PSO Model for Solving Continuous p-median Problem. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_19
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DOI: https://doi.org/10.1007/978-3-319-13817-6_19
Publisher Name: Springer, Cham
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