Investigation of practical, robust and flexible decisions for facility location problems using tabu search and simulation

Theoretical Paper


We investigate how robust and flexible solutions of stochastic capacitated facility location problems (CFLPs) can be obtained by combining metaheuristic optimization with Monte Carlo sampling techniques. To this end, we develop a tabu search procedure for the CFLP, and use this to solve an extensive set of stochastic versions of this problem.


facility location stochastic optimization metaheuristics robustness flexibility 


  1. Beasley JE (1988). An algorithm for solving large capacitated warehouse location problems. Eur J Opl Res 33: 314–325.CrossRefGoogle Scholar
  2. Beasley JE (1990). OR-Library: Distributing test problems by electronic mail. J Opl Res Soc 41: 1069–1072.CrossRefGoogle Scholar
  3. Birge JR (1997). Stochastic programming computation and applications. INFORMS J Computing 9: 111–133.CrossRefGoogle Scholar
  4. Branke J (2001). Evolutionary Optimization in Dynamic Environments. Kluwer: Boston.Google Scholar
  5. Cooper L (1963). Location–allocation problems. Opns Res 11: 331–343.CrossRefGoogle Scholar
  6. Cooper L (1964). Heuristic methods for location–allocation problems. SIAM Rev 6: 37–53.CrossRefGoogle Scholar
  7. Daskin MS (1995). Network and Discrete Location: Models, Algorithms and Applications. John Wiley & Sons: New York.CrossRefGoogle Scholar
  8. Dias LC and Clímaco J (1999). On computing ELECTRE's credibility indices under partial information. J Multi-Criteria Decis Anal 8: 74–92.CrossRefGoogle Scholar
  9. Drezner Z (1987). Heuristic solution methods for two location problems with unreliable facilities. J Opl Res Soc 38: 509–514.CrossRefGoogle Scholar
  10. Erlenkotter D (1978). A dual-based procedure for uncapacitated facility location. Opns Res 26: 992–1009.CrossRefGoogle Scholar
  11. Frank H (1966). Optimum locations on a graph with probabilistic demands. Opns Res 14: 409–421.CrossRefGoogle Scholar
  12. Goetschalckx M, Ahmed S, Shapiro A and Santoso T (2001). Designing flexible and robust supply chains. In: Artiba A (ed). Proceedings of the International Conference on Industrial Engineering and Production Management, Quebec, Canada. Facultés Universitaires Catholiques de Mons: Belgium, pp. 539–551.Google Scholar
  13. Higle JL and Sen S (1991). Stochastic decomposition: An algorithm for two stage stochastic linear programs with recourse. Math Op Res 16: 650–669.CrossRefGoogle Scholar
  14. Hodder JE (1984). Financial market approaches to facility location under uncertainty. Opns Res 32: 1374–1380.CrossRefGoogle Scholar
  15. Infanger G (1994). Planning Under Uncertainty: Solving Large Scale Stochastic Linear Programs. Boyd and Fraser Publishing Co.: Danvers, MA.Google Scholar
  16. Jucker JV and Carlson RC (1976). The simple plant-location problem under uncertainty. Opns Res 24: 1045–1055.CrossRefGoogle Scholar
  17. Kleywegt AJ, Shapiro A and Homem-de Mello T (2001). The sample average approximation method for stochastic discrete optimization. SIAM J Optimization 12: 479–502.CrossRefGoogle Scholar
  18. Kouvelis P and Yu G (1997). Robust Discrete Optimisation and its Applications, Nonconvex Optimization and its Applications, Vol. 14. Kluwer Academic Publishers: Dordrecht.Google Scholar
  19. Law AM and Kelton WD (1999). Simulation Modeling and Analysis. McGraw-Hill: London.Google Scholar
  20. Louveaux FV (1986). Discrete stochastic location models and algorithms. Ann Op Res 6: 23–34.CrossRefGoogle Scholar
  21. Louveaux FV and Peeters D (1992). A dual-based procedure for stochastic facility location. Opns Res 40: 564–573.CrossRefGoogle Scholar
  22. Løkketangen A and Woodruff D (1996). Progressive hedging and tabu search applied to mixed integer (0,1) multi-stage stochastic programming. J Heuristics 2: 111–128.CrossRefGoogle Scholar
  23. Mirchandani PB and Odoni AR (1979). Location of medians on stochastic networks. Transport Sci 13: 85–97.CrossRefGoogle Scholar
  24. Mirchandani PB, Oudjit A and Wong RT (1985). ‘Multidimensional’ extensions and a nested dual approach for the m-median problem. Eur J Opl Res 21: 121–137.CrossRefGoogle Scholar
  25. Mulvey JM, Vanderbei RJ and Zenios SA (1995). Robust optimization of large-scale systems. Opns Res 43: 264–281.CrossRefGoogle Scholar
  26. Norkin VI, Ermoliev YM and Ruszczyínski A (1998a). On optimal allocation of indivisibles under uncertainty. Opns Res 46: 381–395.CrossRefGoogle Scholar
  27. Norkin VI, Pflug GCh and Ruszczyínski A (1998b). A branch and bound method for stochastic global optimization. Math Program 83: 425–450.Google Scholar
  28. Roy B (1998). A missing link in OR-DA: Robustness analysis. Found Comput Decis Sci 23: 141–160.Google Scholar
  29. Sevaux M and Sörensen K (2004). A genetic algorithm for robust schedules in a one-machine environment with ready times and due dates. 4OR 2: 129–147.CrossRefGoogle Scholar
  30. Verweij B et al (2003). The sample average approximation method applied to stochastic routing problems: A computational study. Comput Optim Appl 24: 289–333.CrossRefGoogle Scholar
  31. Vincke P (1999). Robust solutions and methods in decision aid. J Multi-Criteria Decis Anal 8: 181–187.CrossRefGoogle Scholar
  32. Weaver JR and Church RL (1983). Computational procedures for location problems on stochastic networks. Transport Sci 17: 168–180.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan Ltd 2006

Authors and Affiliations

  1. 1.University of AntwerpAntwerpBelgium

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