Iterated Greedy Algorithms for the Maximal Covering Location Problem

  • Francisco J. Rodriguez
  • Christian Blum
  • Manuel Lozano
  • Carlos García-Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7245)

Abstract

The problem of allocating a set of facilities in order to maximise the sum of the demands of the covered clients is known as the maximal covering location problem. In this work we tackle this problem by means of iterated greedy algorithms. These algorithms iteratively refine a solution by partial destruction and reconstruction, using a greedy constructive procedure. Iterated greedy algorithms have been applied successfully to solve a considerable number of problems. With the aim of providing additional results and insights along this line of research, this paper proposes two new iterated greedy algorithms that incorporate two innovative components: a population of solutions optimised in parallel by the iterated greedy algorithm, and an improvement procedure that explores a large neighbourhood by means of an exact solver. The benefits of the proposal in comparison to a recently proposed decomposition heuristic and a standalone exact solver are experimentally shown.

Keywords

iterated greedy algorithm large neighbourhood search maximal covering location problem 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Arakaki, R.G.I., Lorena, L.A.N.: A constructive genetic algorithm for the maximal covering location problem. In: Proceedings of the 4th Metaheuristics International Conference (MIC 2001), pp. 13–17 (2001)Google Scholar
  3. 3.
    Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing 11(6), 4135–4151 (2011)CrossRefGoogle Scholar
  4. 4.
    Church, R., Velle, C.R.: The maximal covering location problem. Papers in Regional Science 32(1), 101–118 (1974)CrossRefGoogle Scholar
  5. 5.
    Culberson, J.C., Luo, F.: Exploring the k-colorable landscape with iterated greedy. Dimacs Series in Discrete Mathematics and Theoretical Computer Science, pp. 245–284. American Mathematical Society (1996)Google Scholar
  6. 6.
    Galvao, R.D., Espejo, L.G.A., Boffey, B.: A comparison of lagrangean and surrogate relaxations for the maximal covering location problem. European Journal of Operational Research 124(2), 377–389 (2000)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Galvao, R.D., ReVelle, C.: A lagrangean heuristic for the maximal covering location problem. European Journal of Operational Research 88(1), 114–123 (1996)MATHCrossRefGoogle Scholar
  8. 8.
    Lorena, L.A., Pereira, M.A.: A lagrangean/surrogate heuristic for the maximal covering location problem using hillsman’s edition. International Journal of Industrial Engineering 9, 57–67 (2001)Google Scholar
  9. 9.
    Megiddo, N., Zemel, E., Hakimi, S.L.: The maximum coverage location problem. SIAM Journal on Algebraic and Discrete Methods 4(2), 253–261 (1983)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Reinelt, G.: The traveling salesman: computational solutions for TSP applications. Springer, Heidelberg (1994)Google Scholar
  11. 11.
    Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research 177(3), 2033–2049 (2007)MATHCrossRefGoogle Scholar
  12. 12.
    Shaw, P.: Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Senne, E.L.F., Pereira, M.A., Lorena, L.A.N.: A decomposition heuristic for the maximal covering location problem. Advances in Operations Research 2010 (2010)Google Scholar
  14. 14.
    Xia, L., Xie, M., Xu, W., Shao, J., Yin, W., Dong, J.: An empirical comparison of five efficient heuristics for maximal covering location problems. In: IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics (SOLI 2009), pp. 747–753 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco J. Rodriguez
    • 1
  • Christian Blum
    • 2
  • Manuel Lozano
    • 1
  • Carlos García-Martínez
    • 3
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.ALBCOM Research GroupTechnical University of CataloniaBarcelonaSpain
  3. 3.Department of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain

Personalised recommendations