Advertisement

Journal of Heuristics

, Volume 6, Issue 3, pp 295–310 | Cite as

Performance of the MOSA Method for the Bicriteria Assignment Problem

  • D. Tuyttens
  • J. Teghem
  • Ph. Fortemps
  • K. Van Nieuwenhuyze
Article

Abstract

The classical linear Assignment problem is considered with two objectives. The aim is to generate the set of efficient solutions. An exact method is first developed based on the two-phase approach. In the second phase a new upper bound is proposed so that larger instances can be solved exactly. The so-called MOSA (Multi-Objective Simulated Annealing) is then recalled; its efficiency is improved by initialization with a greedy approach. Its results are compared to those obtained with the exact method. Extensive numerical experiments have been realized to measure the performance of the MOSA method.

multi-objective programming assignment problem simulated annealing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ben Abdelaziz, F., J. Chaouachi, and S. Krichen (1997). “A Hybrid Heuristic for Multiobjective Knapsack Problems. ” Technical Report, Institut Supérieur de Gestion, Tunisie.Google Scholar
  2. Czyzak, P., M. Hapke, and A. Jaszkiewicz (1994). “Application of the Pareto-Simulated Annealing to the Multiple Criteria Shortest Path Problem. ” Technical Report, Politechnika Poznanska Instytut Informatyki.Google Scholar
  3. Czyzak, P. and A. Jaszkiewicz (1998). “Pareto Simulated Annealing-A Metaheuristic Technique for Multiple Objective Combinatorial Optimization. ” Journal of Multi-Criteria Decision Analysis 7, 34–47.Google Scholar
  4. Gandibleux, X., N. Mezdaoui, and A. Fréville (1996). “Multiobjective Tabu Search Procedure to Solve Combinatorial Optimisation Problems. ” Working Paper, LAMIH-LIMAV, Université de Valenciennes.Google Scholar
  5. Hansen, P. (1997). “Tabu Search for Multiobjective Optimization: MOTS. ” Technical Report, Institute of Mathematical Modelling, Technical University of Denmark.Google Scholar
  6. Pirlot, M. (1996). “General Local Search Methods. ” EJOR 92, 493–511.Google Scholar
  7. Serafini, P. (1992). “Simulated Annealing for Multiple Objective Optimization Problems. ” In Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei, July 1992. vol. 1, pp. 87–96.Google Scholar
  8. Teghem, J. (1996). “Programmation linéaire. ” Editions de l'Université de Bruxelles et Ellipses.Google Scholar
  9. Ulungu, E.L. (1993). “Optimisation combinatoire multicritére: détermination de l'ensemble des solutions efficaces et méthodes interactives. ” Thèse de doctorat, Université de Mons-Hainaut (Belgique).Google Scholar
  10. Ulungu, E.L. and J. Teghem (1994). “Multi-Objective Combinatorial Optimization Problems: A Survey. ” Journal of Multiple-Criteria Decision Analysis 3/2, 83–104.Google Scholar
  11. Ulungu, E.L. and J. Teghem (1995). “The Two Phases Method: An Efficient Procedure to Solve Bi-Objective Combinatorial Optimization Problems. ” Journal Foundations of Computing & Decision Sciences 20(2), 149–165.Google Scholar
  12. Ulungu, E.L., J. Teghem, and Ph. Fortemps (1995). “Heuristics for Multi-Objective Combinatorial Optimization by Simulated Annealing. ” In Multiple Criteria Decision Making: Theory and Applications. Proceeding of the 6th National Conference on Multiple Criteria Decision Making, Beijing, China, Aug. 1995, pp. 228–238.Google Scholar
  13. Ulungu, E.L., J. Teghem, Ph. Fortemps, and D. Tuyttens (1999). “MOSA Method: A Tool for Solving MOCO Problems. ” Journal of Multi-Criteria Decision Analysis 8, 221–236.Google Scholar
  14. Ulungu, E.L., J. Teghem, and Ch. Ost (1998). “Efficiency of Interactive Multi-Objective Simulated Annealing Through a Case Study. ” Journal of the Operational Research Society 49, 1044–1050.Google Scholar
  15. Visée, M., J. Teghem, M. Pirlot, and E.L. Ulungu (1998). “Two-Phases Method and Branch and Bound Procedures to Solve the Bi-Objective Knapsack Problem. ” Journal of Global Optimization 12, 139–155.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • D. Tuyttens
    • 1
  • J. Teghem
    • 2
  • Ph. Fortemps
    • 3
  • K. Van Nieuwenhuyze
    • 4
  1. 1.Laboratory of Mathematics and Operational ResearchFaculté Polytechnique de MonsMonsBelgium
  2. 2.Laboratory of Mathematics and Operational ResearchFaculté, Polytechnique de MonsMonsBelgium
  3. 3.Laboratory of Mathematics and Operational ResearchFaculté, Polytechnique de MonsMonsBelgium
  4. 4.Laboratory of Mathematics and Operational ResearchFaculté, Polytechnique de MonsMonsBelgium

Personalised recommendations