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A Biased-Randomized Algorithm for the Uncapacitated Facility Location Problem

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Applied Mathematics and Computational Intelligence (FIM 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 730))

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Abstract

Facility Location Problems (FLPs) have been widely studied in the fields of Operations Research and Computer Science. This is due to the fact that FLPs have numerous practical applications in different areas, from logistics (e.g., placement of distribution or retailing centers) to Internet computing (e.g., placement of cloud-service servers on a distributed network). In this paper we propose a biased iterated local search algorithm for solving the uncapacitated version of the FLP. Biased randomization of heuristics has been successfully applied in the past to solve other combinatorial optimization problems in logistics, transportation, and production -e.g., different vehicle and arc routing problems as well as scheduling problems. Our approach integrates a biased randomization within an Iterated Local Search framework. Several standard benchmarks from the literature have been used to prove the quality and efficiency of the proposed algorithm.

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References

  1. Alves, M., Almeida, M.: Simulated annealing algorithm for the simple plant location problem: a computational study. Rev. Invest. Operacional 12, 1–31 (1992)

    Google Scholar 

  2. Balinsky, M.L.: On Finding Integer Solutions to Linear Programs (1964)

    Google Scholar 

  3. Bilde, O., Krarup, J.: Sharp lower bounds and efficient algorithms for the simple plant location problem. In: Hammer, P.L., Johnson, E.L., Korte, B.H., Nemhauser, G.L. (eds.) Studies in Integer Programming, Annals of Discrete Mathematics, vol. 1, pp. 79–97. Elsevier (1977)

    Google Scholar 

  4. Cornuejols, G., Nemhauser, G., Wolsey, L.: The uncapacitated facility location problem. In: Mirchandani, P., Francis, R. (eds.) Discrete Location Theory, pp. 119–171. Springer (1990)

    Google Scholar 

  5. Drezner, Z.: Facility Location: A Survey of Applications and Methods. Springer, New York (1995)

    Book  Google Scholar 

  6. Fotakis, D.: Online and incremental algorithms for facility location. SIGACT News 42(1), 97–131 (2011)

    Article  Google Scholar 

  7. Ghosh, D.: Neighborhood search heuristics for the uncapacitated facility location problem. Eur. J. Oper. Res. 150(1), 150–162 (2003). o.R. Applied to HealthServices

    Article  MathSciNet  MATH  Google Scholar 

  8. Gonzalez, S., Riera, D., Juan, A., Elizondo, M., Fonseca, P.: Sim-RandSHARP: a hybrid algorithm for solving the arc routing problem with stochastic demands. In: Proceedings of the 2012 Winter Simulation Conference (WSC), pp. 1–11, December 2012

    Google Scholar 

  9. González-Martín, S., Juan, A.A., Riera, D., Castellà, Q., Muñoz, R., Pérez, A.: Development and assessment of the SHARP and RandSHARP algorithms for the arc routing problem. AI Commun. 25(2), 173–189 (2012)

    MathSciNet  Google Scholar 

  10. Hoefer, M.: (2014). http://www.mpi-inf.mpg.de/departments/d1/projects/benchmarks/UflLib/

  11. Juan, A., Faulin, J., Jorba, J., Riera, D., Masip, D., Barrios, B.: On the use of monte carlo simulation, cache and splitting techniques to improve the clarke and wright savings heuristics. J. Oper. Res. Soc. 62, 1085–1097 (2011)

    Article  Google Scholar 

  12. Kant, G., Jacks, M., Aantjes, C.: Coca-Cola enterprises optimizes vehicle routes for efficient product delivery. Interfaces 38(1), 40–50 (2008)

    Article  Google Scholar 

  13. Kochetov, Y., Ivanenko, D.: Computationally difficult instances for the uncapacitated facility location problem. In: Ibaraki, T., Nonobe, K., Yagiura, M. (eds.) Metaheuristics: Progress as Real Problem Solvers, Operations Research/Computer Science Interfaces Series, vol. 32, pp. 351–367. Springer, US (2005)

    Google Scholar 

  14. Kuehn, A.A., Hamburger, M.J.: A heuristic program for locating warehouses. Manag. Sci. 9(4), 643–666 (1963)

    Article  Google Scholar 

  15. Lai, M.C., suk Sohn, H., Tseng, T.L.B., Chiang, C.: A hybrid algorithm for capacitated plant location problem. Expert Syst. Appl. 37(12), 8599–8605 (2010)

    Article  Google Scholar 

  16. Lee, G., Murray, A.T.: Maximal covering with network survivability requirements in wireless mesh networks. Comput. Environ. Urban Syst. 34(1), 49–57 (2010)

    Article  Google Scholar 

  17. Lourenço, H., Martin, O., Stuetzle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, International Series in Operations Research and Management Science, vol. 146, pp. 363–397. Springer, US (2010)

    Google Scholar 

  18. Marić, M., Stanimirović, Z., Boz̆ović, S.: Hybrid metaheuristic method for determining locations for long-term health care facilities. Ann. Oper. Res. 227(1), 3–23 (2013)

    MathSciNet  MATH  Google Scholar 

  19. Resende, M.G., Werneck, R.F.: A hybrid multistart heuristic for the uncapacitated facility location problem. Eur. J. Oper. Res. 174(1), 54–68 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  20. Resende, M., Ribeiro, C.: Greedy randomized adaptive search procedures: advances, hybridizations, and applications. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, International Series in Operations Research and Management Science, vol. 146, pp. 283–319. Springer, US (2010)

    Google Scholar 

  21. Snyder, L.V.: Facility location under uncertainty: a review. IIE Trans. 38(7), 547–564 (2006)

    Article  Google Scholar 

  22. Stollsteimer, J.: The Effect of Technical Change and Output Expansion on the Optimum Number, Size, and Location of Pear Marketing Facilities in a California Pear Producing Region. University of California, Berkeley (1961)

    Google Scholar 

  23. Sun, M.: Solving the uncapacitated facility location problem using tabu search. Comput. Oper. Res. 33(9), 2563–2589 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  24. Thouin, F., Coates, M.: Equipment allocation in video-on-demand network deployments. ACM Trans. Multimedia Comput. Commun. Appl. 5(1), 5:1–5:22 (2008)

    Article  Google Scholar 

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Acknowledgments

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P) and FEDER. Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (2014-CTP-00001).

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Authors and Affiliations

  1. Computer Science Department, IN3 - Open University of Catalonia, Barcelona, Spain

    Jesica de Armas, Angel A. Juan & Joan Manuel Marquès

Authors
  1. Jesica de Armas
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  2. Angel A. Juan
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  3. Joan Manuel Marquès
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Corresponding author

Correspondence to Jesica de Armas .

Editor information

Editors and Affiliations

  1. Department of Business, University of Barcelona, Barcelona, Spain

    Anna M. Gil-Lafuente

  2. Department of Management Control and Information Systems, University of Chile, Santiago, Chile

    José M. Merigó

  3. Department of Mathematics, University of Delhi, Delhi, India

    Bal Kishan Dass

  4. Department of Applied Sciences, Delhi Technical Campus, Greater Noida, Uttar Pradesh, India

    Rajkumar Verma

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de Armas, J., Juan, A.A., Marquès, J.M. (2018). A Biased-Randomized Algorithm for the Uncapacitated Facility Location Problem. In: Gil-Lafuente, A., Merigó, J., Dass, B., Verma, R. (eds) Applied Mathematics and Computational Intelligence. FIM 2015. Advances in Intelligent Systems and Computing, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-75792-6_22

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  • DOI: https://doi.org/10.1007/978-3-319-75792-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75791-9

  • Online ISBN: 978-3-319-75792-6

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