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How to Locate Disperse Obnoxious Facility Centers?

  • Jesús Sánchez-Oro
  • J. Manuel ColmenarEmail author
  • Enrique García-Galán
  • Ana D. López-Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11328)

Abstract

The bi-objective obnoxious p-median problem has not been extensively studied in the literature yet, even having an enormous real interest. The problem seeks to locate p facilities but maximizing two different objectives that are usually in conflict: the sum of the minimum distance between each customer and their nearest facility center, and the dispersion among facilities, i.e., the sum of the minimum distance from each facility to the rest of the selected facilities. This problem arises when the interest is focused on locating obnoxious facilities such as waste or hazardous material, nuclear power or chemical plants, noisy or polluting services like airports. To address the bi-objective obnoxious p-median problem we propose a variable neighborhood search approach. Computational experiments show promising results. Specifically, the proposed algorithm obtains high-quality efficient solutions compared to the state-of-art efficient solutions.

Keywords

Location problem Obnoxious p-median problem Multi-objective optimization Variable neighborhood search 

References

  1. 1.
    Carlsson, J.G., Jia, F.: Continuous facility location with backbone network costs. Transp. Sci. 49(3), 433–451 (2014)CrossRefGoogle Scholar
  2. 2.
    Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation. Springer, New York (2006)zbMATHGoogle Scholar
  3. 3.
    Colmenar, J., Martí, R., Duarte, A.: Multi-objective memetic optimization for the bi-objective obnoxious p-median problem. Knowl.-Based Syst. 144, 88–101 (2018)CrossRefGoogle Scholar
  4. 4.
    Cornuéjols, G., Nemhauser, G., Wolsey, L.: The uncapacitated facility location problem. In: Mirchandani, P.B., Francis, R.L. (eds.) Discrete Location Theory, pp. 119–171. Wiley-Interscience, New York (1990)Google Scholar
  5. 5.
    Dantrakul, S., Likasiri, C., Pongvuthithum, R.: Applied p-median and p-center algorithms for facility location problems. Expert Syst. Appl. 41(8), 3596–3604 (2014)CrossRefGoogle Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Duarte, A., Pantrigo, J., Pardo, E., Sánchez-Oro, J.: Parallel variable neighbourhood search strategies for the cutwidth minimization problem. IMA J. Manag. Math. 27(1), 55 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Duarte, A., Pantrigo, J.J., Pardo, E.G., Mladenovic, N.: Multi-objective variable neighborhood search: an application to combinatorial optimization problems. J. Glob. Optim. 63(3), 515–536 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Farahani, R.Z., Hekmatfar, M.: Facility Location: Concepts, Models, Algorithms and Case Studies. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6(2), 109–133 (1995)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hansen, P., Mladenović, N., Todosijević, R., Hanafi, S.: Variable neighborhood search: basics and variants. EURO J. Comput. Optim. 5(3), 423–454 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Marín, A.: The discrete facility location problem with balanced allocation of customers. Eur. J. Oper. Res. 210(1), 27–38 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Sánchez-Oro, J., Sevaux, M., Rossi, A., Martí, R., Duarte, A.: Solving dynamic memory allocation problems in embedded systems with parallel variable neighborhood search strategies. Electron. Notes Discret Math. 47, 85–92 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wu, L.Y., Zhang, X.S., Zhang, J.L.: Capacitated facility location problem with general setup cost. Comput. Oper. Res. 33(5), 1226–1241 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100. International Center for Numerical Methods in Engineering (CIMNE) (2002)Google Scholar
  17. 17.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Rey Juan Carlos UniversityMóstolesSpain
  2. 2.Pablo de Olavide UniversitySevillaSpain

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