Advertisement

Solving multi-objective scheduling problems—An integrated systems approach

  • Martin Josef Geiger
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 217)

Abstract

In the past, numerous approaches have been formulated either for approximating Pareto-optimal alternatives or supporting the decision making process with an interactive multi criteria decision aiding methodology. The article on the other hand presents an integrated system for the resolution of problems under multiple objectives, combining both aspects. A method base of metaheuristics is made available for the identification of optimal alternatives of machine scheduling problems, and the selection of a most preferred solution is supported in an interactive decision making procedure.

As the system is aimed at end users, a graphical interface allows the easy adaptation of metaheuristic techniques. Contrary to existing soft-ware class libraries, the system therefore enables users with little or no knowledge in the mentioned areas to successfully solve scheduling problems and customize and test metaheuristics.

After successfully competing in the finals in Ronneby (Sweden), the software has been awarded the European Academic Software Award 2002 (http://www.easa-award.net/, http://www.bth.se/11ab/easa_2002.nsf).

Key words

Multi-Objective Optimization Multi-Objective Metaheuristics Decision Support System Scheduling 

References

  1. 1.
    Tapan P. Bagchi. Multiobjective scheduling by genetic algorithms. Kluwer Academic Publishers, Boston, Dordrecht, London, 1999.zbMATHGoogle Scholar
  2. 2.
    Matthieu Basseur, Franck Seynhaeve, and El-ghazali Talbi. Design of multi-objective evolutionary algorithms: Application to the flow-shop scheduling problem. In Congress on Evolutionary Computation (CEC’2002), volume 2, pages 1151–1156, Piscataway, NJ, May 2002. IEEE Service Center.Google Scholar
  3. 3.
    J. E. Beasley. Obtaining test problems via internet. Journal of Global Optimization, 8:429–433, 1996.zbMATHCrossRefGoogle Scholar
  4. 4.
    J. Blazewicz, K. H. Ecker, E. Pesch, G. Schmidt, and J. Weglarz. Scheduling Computer and Manufacturing Processes. Springer Verlag, Berlin, Heidelberg, New York, 2. edition, 2001.zbMATHGoogle Scholar
  5. 5.
    R. W. Conway, W. L. Maxwell, and L. W. Miller. Theory of Scheduling. Addison-Wesley, Reading, MA, 1967.zbMATHGoogle Scholar
  6. 6.
    Richard L. Daniels. Incorporating preference information into multi-objective scheduling. European Journal of Operational Research, 77:272–286, 1994.zbMATHCrossRefGoogle Scholar
  7. 7.
    Richard L. Daniels and Joseph B. Mazzola. A tabu-search heuristic for the flexible-resource flow shop scheduling problem. Annals of Operations Research, 41:207–230, 1993.zbMATHCrossRefGoogle Scholar
  8. 8.
    Carlos M. Fonseca and Peter J. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, CA, 1993. Morgan Kaufmann Publishers.Google Scholar
  9. 9.
    Tomáš Gál, Theodor J. Stewart, and Thomas Hanne, editors. Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications, volume 21 of International Series in Operations Research & Management Science. Kluwer Academic Publishers, Boston, Dordrecht, London, 1999.zbMATHGoogle Scholar
  10. 10.
    Henry L. Gantt. Efficiency and democracy. Transactions of the American Society of Mechanical Engineers, 40:799–808, 1919.Google Scholar
  11. 11.
    B. Giffler and G. L. Thompson. Algorithms for solving production-scheduling problems. Operations Research, 8:487–503, 1960.zbMATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    R. Haupt. A survey of priority rule-based scheduling. Operations Research Spektrum, 11(1):3–16, 1989.zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Sang M. Lee and David L. Olson. Goal programming. In Theodor J. Stewart, and Thomas Hanne, editors. Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications, volume 21 of International Series in Operations Research & Management Science. Kluwer Academic Publishers, Boston, Dordrecht, London, 1999 Gál et al. [9], chapter 8, pages 8.1–8.33.Google Scholar
  14. 14.
    V. Lotfi, T. J. Stewart, and S. Zionts. An aspiration-level interactive model for multiple criteria decision making. Computers & Operations Research, 19(7):671–681, 1992.zbMATHCrossRefGoogle Scholar
  15. 15.
    Michael Pinedo. Planning and Scheduling in Manufacturing and Services. Springer Verlag, Berlin, Heidelberg, New York, 2005.zbMATHGoogle Scholar
  16. 16.
    Colin R. Reeves. Landscapes, operators and heuristic search. Annals of Operations Research, 86:473–490, 1999.zbMATHMathSciNetCrossRefGoogle Scholar
  17. 17.
    Eric Taillard. Benchmarks for basic scheduling problems. European Journal of Operational Research, 64:278–285, 1993.zbMATHCrossRefGoogle Scholar
  18. 18.
    Vincent T’kindt and Jean-Charles Billaut. Multicriteria Scheduling: Theory, Models and Algorithms. Springer Verlag, Berlin, Heidelberg, New York, 2002.zbMATHGoogle Scholar
  19. 19.
    E. L. Ulungu, J. Teghem, P. H. Fortemps, and D. Tuyttens. MOSA method: A tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Making, 8:221–236, 1999.zbMATHCrossRefGoogle Scholar
  20. 20.
    David A. Van Veldhuizen and Gary B. Lamont. Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Computation, 8(2):125–147, 2000.CrossRefGoogle Scholar
  21. 21.
    Philippe Vincke. Multicriteria Decision-Aid. John Wiley & Sons, Chichester, New York, Brisbane, Toronto, Singapore, 1992.Google Scholar
  22. 22.
    Darrell Whitley. Permutations. In Thomas Bäck, David B. Fogel, and Zbigniew Michalewicz, editors, Handbook of Evolutionary Computation, chapter C3.3.3, pages C3.3:14–C3.3.20. Institute of Physics Publishing, Bristol, 1997.Google Scholar
  23. 23.
    Andrzej P. Wierzbicki. Reference point approaches. In Theodor J. Stewart, and Thomas Hanne, editors. Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications, volume 21 of International Series in Operations Research & Management Science. Kluwer Academic Publishers, Boston, Dordrecht, London, 1999 Gál et al. [9], chapter 9, pages 9.1–9.39.Google Scholar
  24. 24.
    James M. Wilson. Gantt charts: A centenary appreciation. European Journal of Operational Research, 149:430–437, 2003.zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Martin Josef Geiger
    • 1
  1. 1.Lehrstuhl für IndustriebetriebslehreUniversität HohenheimStuttgartGermany

Personalised recommendations