Improving solution of discrete competitive facility location problems
- 323 Downloads
We consider discrete competitive facility location problems in this paper. Such problems could be viewed as a search of nodes in a network, composed of candidate and customer demand nodes, which connections correspond to attractiveness between customers and facilities located at the candidate nodes. The number of customers is usually very large. For some models of customer behavior exact solution approaches could be used. However, for other models and/or when the size of problem is too high to solve exactly, heuristic algorithms may be used. The solution of discrete competitive facility location problems using genetic algorithms is considered in this paper. The new strategies for dynamic adjustment of some parameters of genetic algorithm, such as probabilities for the crossover and mutation operations are proposed and applied to improve the canonical genetic algorithm. The algorithm is also specially adopted to solve discrete competitive facility location problems by proposing a strategy for selection of the most promising values of the variables in the mutation procedure. The developed genetic algorithm is demonstrated by solving instances of competitive facility location problems for an entering firm.
KeywordsCompetitive facility location Discrete optimization Genetic algorithm
This research was funded by a grant (No. MIP-051/2014) from the Research Council of Lithuania.
- 2.Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS ’95, pp. 39–43 (1995). doi: 10.1109/MHS.1995.494215
- 7.Hakimi, L.: Locations with spatial interactions: competitive locations and games. In: Mirchandani, P.B., Francis, R.L. (eds.) Discrete Location Theory, pp. 439–478. Wiley, New York (1990)Google Scholar
- 8.Hakimi, L.: Location with spatial interactions: competitive locations and games. In: Drezner, Z. (ed.) Facility Location: A Survey of Applications and Methods, pp. 367–386. Springer, Berlin (1995)Google Scholar
- 9.Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
- 15.Shaikh, A., Salhi, S., Ndiaye, M.: New MAXCAP related problems: formulation and model resolution. Comput. Ind. Eng. 85(3), 817–848 (2015)Google Scholar