Parallel Shared-Memory Multi-Objective Stochastic Search for Competitive Facility Location

  • Algirdas Lančinskas
  • Pilar Martínez Ortigosa
  • Julius Žilinskas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8805)


A stochastic search algorithm for local multi-objective optimization is developed and applied to solve a multi-objective competitive facility problem for firm expansion using shared-memory parallel computing systems. The performance of the developed algorithm is experimentally investigated by solving competitive facility location problems, using up to 16 shared-memory processing units. It is shown that the developed algorithm has advantages against its precursor in the sense of the precision of optimization and that it has almost linear speed-up on 16 shared-memory processing units, when solving competitive facility location problems of different scope reasonable for practical applications.


Facility Location Multi-Objective Optimization Stochastic Search Shared Memory Parallel Computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  2. 2.
    Drezner, Z., Klamroth, K., Schobel, A., Wesolowsky, G.O.: The Weber problem. In: Drezner, Z., Hamacher, H. (eds.) Facility Location: Applications and Theory, pp. 1–36. Springer, Berlin (2002)Google Scholar
  3. 3.
    Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S.: Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega 45(0), 92–118 (2014)CrossRefGoogle Scholar
  4. 4.
    Fernández, J., Pelegrín, B., Plastria, F., Tóth, B.: Planar location and design of a new facility with inner and outer competition: An interval lexicographical-like solution procedure. Networks and Spatial Economics 7(1), 19–44 (2007)CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Friesz, T., Miller, T., Tobin, R.: Competitive networks facility location models: a survey. Papers in Regional Science 65, 47–57 (1998)CrossRefGoogle Scholar
  6. 6.
    Huapu, L., Jifeng, W.: Study on the location of distribution centers: A bi-level multi-objective approach. In: Logistics, pp. 3038–3043. American Society of Civil Engineers (2009)Google Scholar
  7. 7.
    Lančinskas, A., Ortigosa, P.M., Žilinskas, J.: Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm. Nonlinear Analysis: Modelling and Control 18(3), 293–313 (2013)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Lančinskas, A., Žilinskas, J.: Approaches to parallelize pareto ranking in NSGA-II algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011, Part II. LNCS, vol. 7204, pp. 371–380. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Lančinskas, A., Żilinskas, J.: Solution of multi-objective competitive facility location problems using parallel NSGA-II on large scale computing systems. In: Manninen, P., Öster, P. (eds.) PARA. LNCS, vol. 7782, pp. 422–433. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Lančinskas, A., Žilinskas, J.: Parallel multi-objective memetic algorithm for competitive facility location. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013, Part II. LNCS, vol. 8385, pp. 354–363. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Medaglia, A.L., Villegas, J.G., Rodrguez-Coca, D.M.: Hybrid biobjective evolutionary algorithms for the design of a hospital waste management network. Journal of Heuristics 15(2), 153–176 (2009)CrossRefzbMATHGoogle Scholar
  12. 12.
    Plastria, F.: Static competitive facility location: An overview of optimisation approaches. European Journal of Operational Research 129(3), 461–470 (2001)CrossRefMathSciNetzbMATHGoogle Scholar
  13. 13.
    Redondo, J.L., Fernández, J., Álvarez, J.D., Arrondoa, A.G., Ortigosa, P.M.: Approximating the Pareto-front of continuous bi-objective problems: Application to a competitive facility location problem. In: Casillas, J., Martínez-López, F.J., Corchado, J.M. (eds.) Management of Intelligent Systems. AISC, vol. 171, pp. 207–216. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    ReVelle, C., Eiselt, H., Daskin, M.: A bibliography for some fundamental problem categories in discrete location science. European Journal of Operational Research 184(3), 817–848 (2008)CrossRefMathSciNetzbMATHGoogle Scholar
  15. 15.
    Villegas, J., Palacios, F., Medaglia, A.: Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example. Annals of Operations Research 147, 109–141 (2006)CrossRefMathSciNetzbMATHGoogle Scholar
  16. 16.
    Weber, A.: Theory of the Location of Industries. University of Chicago Press (1929)Google Scholar
  17. 17.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K.C., Tsahalis, D.T., Périaux, J., Papailiou, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100 (2001)Google Scholar
  18. 18.
    Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms – a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Algirdas Lančinskas
    • 1
  • Pilar Martínez Ortigosa
    • 2
  • Julius Žilinskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania
  2. 2.Universidad de Almería, ceiA3AlmeríaSpain

Personalised recommendations