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).
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Tapan P. Bagchi. Multiobjective scheduling by genetic algorithms. Kluwer Academic Publishers, Boston, Dordrecht, London, 1999.
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.
J. E. Beasley. Obtaining test problems via internet. Journal of Global Optimization, 8:429–433, 1996.
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.
R. W. Conway, W. L. Maxwell, and L. W. Miller. Theory of Scheduling. Addison-Wesley, Reading, MA, 1967.
Richard L. Daniels. Incorporating preference information into multi-objective scheduling. European Journal of Operational Research, 77:272–286, 1994.
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.
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.
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.
Henry L. Gantt. Efficiency and democracy. Transactions of the American Society of Mechanical Engineers, 40:799–808, 1919.
B. Giffler and G. L. Thompson. Algorithms for solving production-scheduling problems. Operations Research, 8:487–503, 1960.
R. Haupt. A survey of priority rule-based scheduling. Operations Research Spektrum, 11(1):3–16, 1989.
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.
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.
Michael Pinedo. Planning and Scheduling in Manufacturing and Services. Springer Verlag, Berlin, Heidelberg, New York, 2005.
Colin R. Reeves. Landscapes, operators and heuristic search. Annals of Operations Research, 86:473–490, 1999.
Eric Taillard. Benchmarks for basic scheduling problems. European Journal of Operational Research, 64:278–285, 1993.
Vincent T’kindt and Jean-Charles Billaut. Multicriteria Scheduling: Theory, Models and Algorithms. Springer Verlag, Berlin, Heidelberg, New York, 2002.
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.
David A. Van Veldhuizen and Gary B. Lamont. Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Computation, 8(2):125–147, 2000.
Philippe Vincke. Multicriteria Decision-Aid. John Wiley & Sons, Chichester, New York, Brisbane, Toronto, Singapore, 1992.
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.
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.
James M. Wilson. Gantt charts: A centenary appreciation. European Journal of Operational Research, 149:430–437, 2003.
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Geiger, M.J. (2006). Solving multi-objective scheduling problems—An integrated systems approach. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice. IFIP AI 2006. IFIP International Federation for Information Processing, vol 217. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34747-9_51
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