Abstract
The Job-Shop scheduling problem constitutes a typical NP-Difficult problem. Determining an optimal solution is almost impossible, but trying to improve an existent solution is the way to lead to a tasks repartition which is better. We use Multi-Agents Systems (M.A.S.). These simulate the behavior of entities that are going to collaborate to accomplish actions on a Gantt diagram with the intention to better resolve a given economic function. Communications between global and local agents, components of the MAS, due to their actions, manage the appearance of agents of intermediate granularity and the global optimization in production scheduling. To have micro and meta-agents, a Multi-Objective Genetic algorithm is used, and especially on account of an ideal solution of such a problem which is a point where each objective function corresponds to the best (minimum) possible value. The genetic autonomy and the notion of motivation for an agent may lead to a drastically new kind of emergence phenomenon (different social behavior, auto-referring evaluation process, ...) in self-organizing multiagent systems. It is certainly a difficult task but it may set the seeds of a prolific approach concerning artificial life to optimize a Job-Shop Scheduling Problem.
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References
Bäck, T.:”, Self-Adaptation in Genetic Algorithms, [19] (1992) 263–271 81
Cardon, A. and Lesage, F.: An Interpretation Process of Communication Between Actors in a Distributed System for Crisis Management, Proceedings of the Symposium of Informatic Economics, (1997) 82
Cardon, A. and Durand, S.: A Model of Crisis Management System Including Mental Representations, AAAI Spring Symposium, Stanford University, USA, March (1997)
Chankong, V. and Haines, Y. Y.: Multiobjective decision making: theory and methodology, North Holland series in system science and engineering, North Holland, New York, 8, (1983) 81
Ferber, J.: Les systèmes multi-agents, Vers une intelligence collective, Ed. InterEditions, Paris (1995)
Gass, S. and Saaty, T.: The computational algorithm for the parametric objective function, Naval research logistics quarterly, 2 (1955) 39–65 81
Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA (1989) 81
Holland, J. H.: Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI (1975) 81
Holland, J. H.: Adaptation in natural and artificial systems: an introductory analysis with applications in biology, control and artificial intelligence, Complex adaptive systems, MIT Press, Cambridge, MA (1992) 81
Lamy, P.: Ordonnancement et gestion de production, Hermes (1987) 77
Langton, C. G.: Artificial Life: the proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, Workshop held September, 1987 in Los Alamos, New Mexico, Addison-Wesley, Redwood City, CA, September (1989) 77
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, Springer, Berlin (1992) 80
Pécuchet, JP. & al: FISIAS, Fast Interactive System for Intelligent Automated Scheduling, Revue Automatique et Productique Appliquées, Hermes, 2(4), (1989) 23–38 77
Poloni, C.: Hybrid GA for multi objective aerodynamic shape optimisation, Genetic Algorithms in Engineering and Computer Science, by Winter G., Périaux J., Gálan M., Cuesta P., John Wiley & Sons Ltd, England, December (1995) 397–416 81, 82
Portmann, MC.: Méthodes de décomposition spatiale et temporelle en ordonnancement de production, Université de Nancy 1, Doctorat d’Etat, (1987) 77
Vacher, JP., Galinho, T. and Z. Mammeri, Z.: Une application des algorithmes génétiques à l’ordonnancement d’atelier, MOSIM’97, Actes de la première conférence francophone, Modélisation et simulation des systèmes de production et de logistique, by M. Itmi, JP. Pécuchet, H. Pierreval, Editions Hermes, Paris, (1997) 43–50 82
Vacher, JP., Cardon, A. and Galinho, T.: Genetic Algorithms in a Multi-Agent System, Proceeding of the EUROGEN’97 Conference, Trieste, Italy, Web site: http://eurogen.cira.it, (1997) 82
Vacher, JP., Cardon, A. and Galinho, T.: Heuristics Granularity in a Multi-Agents Systems (MAS): Application to the Production Management, Proceeding of the YOR10 conference, (1998) 82
Varela, F. J. and Bourgine P.: Towards a Pratice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, MIT Press, Cambridge, MA (1992) 86
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Galinho, T., Cardon, A., Vacher, JP. (1998). Genetic Integration in a Multiagent System for Job-Shop Scheduling. In: Coelho, H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science(), vol 1484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49795-1_7
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DOI: https://doi.org/10.1007/3-540-49795-1_7
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