Operational Research

, Volume 7, Issue 3, pp 323–343 | Cite as

Hybrid heuristics for the probabilistic maximal covering location-allocation problem

  • Francisco de Assis Corrêa
  • Antonio Augusto Chaves
  • Luiz Antonio Nogueira Lorena


The Maximal Covering Location Problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhance its applicability, like the probabilistic model for the maximum covering location-allocation with constraint in waiting time or queue length for congested systems, with one or more servers per service center. This paper presents one solution procedure for that probabilistic model, considering one server per center, using a Hybrid Heuristic known as Clustering Search (CS), that consists of detecting promising search areas based on clustering. The computational tests provide results for network instances with up to 818 vertices.


Location problems covering problems congested systems clustering search 


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  1. Brotcorne L., Laporte G. and Semet F. (2003). Ambulance Location and relocation models. European Journal of Operational Research, 147, pp. 451–463.CrossRefGoogle Scholar
  2. Chaves A. A. and Lorena L. A. N. (2005). Hybrid algorithms with detection of promising areas for the prize collecting traveling salesman problem. Fifth international conference on hybrid intelligent systems (HIS’05), pp. 49–54.Google Scholar
  3. Chung C.H. (1986). Recent applications of the Maximal Covering Location Problem (MCLP) model. Journal of the Operational Research Society. 37, pp. 735–746.CrossRefGoogle Scholar
  4. Church R.L. and ReVelle C. (1974). Maximal covering location problem. Papers of the Regional Science Association, 32, pp. 101–118.CrossRefGoogle Scholar
  5. Corrêa F.A. and Lorena L.A.N. (2006). Using the Constructive Genetic Algorithm for Solving the Probabilistic Maximal Covering Location-Allocation Problem. I Workshop on Computational Intelligence/SBRN. Available at Scholar
  6. Current J. R. and O’Kelly M. (1981). Locating emergency warning sirens. Decision Sciences, 23, pp. 221–234.CrossRefGoogle Scholar
  7. Daskin M. S. (1995). Network and discrete location: models, algorithms and applications. John Wiley & Sons, New York.Google Scholar
  8. Eaton D., Hector M., Sanchez V., Latingua R. and Morgan J. (1986). Determining ambulance deployment in Santo Domingo, Dominican Republic. Journal of the Operational Research Society, 37, pp. 113–126.CrossRefGoogle Scholar
  9. Feo T. and Resende M. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6, pp. 109–133.CrossRefGoogle Scholar
  10. Galvão R.D. (2004). Uncapacitated facility location problems: contributions. Pesquisa Operacional, 24: 7–38.CrossRefGoogle Scholar
  11. Galvão R. D. and ReVelle C. S. (1996). A Lagrangean heuristic for the maximal covering location problem. European Journal of Operational Research, 88, pp. 114–123.CrossRefGoogle Scholar
  12. Glover F. (1996). Tabu search and adaptive memory programming: Advances, applications and challenges. Interfaces in Computer Science and Operations Research, pp. 1–75.Google Scholar
  13. Hale T. S. and Moberg C. R. (2003). Location science review. Annals of Operations Research, 123, pp. 21–35.CrossRefGoogle Scholar
  14. Hougland E. S. and Stephens N. T. (1976). Air pollulant monitor sitting by analytical techniques. Journal of the Air Pollution Control Association, 26, pp. 52–53.Google Scholar
  15. ILOG CPLEX 10.0: User’s Manual, France, 2006.Google Scholar
  16. Larson R. C. and Odoni A. R. (1981). Urban operations research, Prentice Hall, Englewood Cliffs, N.J.Google Scholar
  17. Lorena L. A. N. and Furtado J. C. (2001). Constructive genetic algorithm for clustering problems. 〈〉 Evolutionary Computation 〈〉, 9(3), pp. 309–327.Google Scholar
  18. Lorena L. A. N. and Pereira M. A. (2002). A lagrangean/surrogate heuristic for the maximal covering location problem using Hillsman’s edition, International Journal of Industrial Engineering, 9, pp. 57–67.Google Scholar
  19. Marianov V. and Serra D. (1998). Probabilistic maximal covering location-allocation models for congested systems. Journal of Regional Science, 38(3): 401–424.CrossRefGoogle Scholar
  20. Marianov V. and Serra D. (2001). Hierarchical location-allocation models for congested systems. European Journal of Operational Research, 135: 195–208.CrossRefGoogle Scholar
  21. Moore G. C. and ReVelle C.S. (1982). The Hierarchical Service Location Problem, Management Science, 28 (7), pp. 775–780.CrossRefGoogle Scholar
  22. Oliveira A. C. M. and Lorena L. A. N. (2004). Detecting-promising areas by evolutionary clustering search. Bazzan, A. L. C. and Labidi, S. (Eds.) Springer Lecture Notes in Artificial Intelligence Series vol. 3171, pp. 385–394.Google Scholar
  23. Oliveira A. C. M. and Lorena L. A. N. (2005). Population training heuristics 〈〉. Gottlieb, J. and Raidl, G. (Eds.) Springer Lecture Notes in Computer Science Series Vol. 3448, pp. 166–176.Google Scholar
  24. Oliveira A. C. M. and Lorena L. A. N. (2007). Hybrid Evolutionary Algorithms and Clustering Search. Crina Grosan, Ajith Abraham and Hisao Ishibuchi (Eds.) Springer SCI Series, vol. 75, pp. 81–102.Google Scholar
  25. Pereira M. A., Lorena L. A. N. and Senne E. L. F. (2007). A column generation approach for the maximal covering location problem. International Transactions in Operations Research, v. 14, p. 349–364.CrossRefGoogle Scholar
  26. Pirkul H. and Schilling D. A. (1991). The maximal covering location problem with capacities on total workload. Management Science, 37(2), pp. 233–248.CrossRefGoogle Scholar
  27. Serra D. and Marianov V. (2004). New trends in public facility location modeling. Universitat Pompeu Fabra Economics and Business Working Paper 755. Available at 〈〉.Google Scholar

Copyright information

© Hellenic Operational Research Society 2007

Authors and Affiliations

  • Francisco de Assis Corrêa
    • 1
  • Antonio Augusto Chaves
    • 1
  • Luiz Antonio Nogueira Lorena
    • 1
  1. 1.LAC C Computer and Applied Mathematics LaboratoryINPE C Brazilian Space Research InstituteSão José dos Campos C SPBrazil

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