Evolutionary algorithms for job-shop scheduling

• Autorzy: Mesghouni K. , Hammadi S. , Borne P.
• Języki publikacji: EN

Abstrakty

EN

This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).

Słowa kluczowe

EN

job-shop scheduling; evolutionary algorithms; parallel representation

PL

harmonogramowanie produkcji; algorytm ewolucyjny; reprezentacja równoległa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: Vol. 14, no 1
• Strony: 91--103
• Opis fizyczny: Bibliogr. 20 poz., tab., wykr.

Twórcy

autor

Mesghouni K.

• Ecole Centrale de Lille, LAIL - UMR 8021 BP 48, 59651 Villeneuve d’Ascq Cedex, France

autor

Hammadi S.

• Ecole Centrale de Lille, LAIL - UMR 8021 BP 48, 59651 Villeneuve d’Ascq Cedex, France

autor

Borne P.

• Ecole Centrale de Lille, LAIL - UMR 8021 BP 48, 59651 Villeneuve d’Ascq Cedex, France

Bibliografia

• [1] Baghi S., Uckun S., Miyab Y. and Kawamura K. (1991): Exploring problem-specific recombination operators for job shop scheduling. — Proc. 4-th Int. Conf. Genetic Algorithms, University of California, San Diego, pp. 10–17, July 13–16.
• [2] Banzhaf W., Nordin P., Keller R.E. and Francone F.D. (1998): Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and Its Application. — San Francisco: Morgan Kaufmann.
• [3] Bruns R. (1993): Direct chromosome representation and advanced genetic operators for production scheduling. — Proc. 5-th Int. Conf. Genetic Algorithms, University of Illinois at Urbana-Champaign, pp. 352–359.
• [4] Carlier J. and Chretienne P. (1988): Problèmes d’ordonnancement: Modélisation / complexité / algorithmes.— Paris: Masson.
• [5] Croce F., Tadei R. and Volta G. (1995): A genetic algorithm for the job shop problem. — Comp. Opers. Res., Vol. 22, No. 1, pp. 15–24.
• [6] Dasgupta D. and Michalewicz Z. (1997): Evolutionary Algorithms in Engineering Applications. — Berlin: Springer-Verlag.
• [7] Della Croce F., Tadei R. and Volta G. (1995): A Genetic Algorithm for Job Shop Problem. — Comput. Ops. Res., Vol. 22, No. 1, pp. 15–24.
• [8] Fonseca C.M. and Fleming P.J. (1998): Multiobjective optimization and multiple constraint handling with evolutionary algorithms, Part I: Unified formulation. — IEEE Trans/SMC, Part A: Syst. Hum., Vol. 28, No. 1, pp. 26–37.
• [9] Garey M.R. and Johnson D.S. (1979): Computers and Intractability: A Guide to Theory of NP-Completeness. — New York: W.H. Freeman and Co.
• [10] Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning. — Massachusetts: Addison Wesley.
• [11] Golver F., Taillard E., De Werra D. (1993): A user’s guide to tabu search.—Ann. Opers. Res., Vol. 41, No. 1, pp. 3–28.
• [12] Kirkpatrick S., Gelatt C.D. and Vecchi M.P. (1983): Optimization by simulated annealing. — Science, Vol. 220, No. 4598, pp. 671–680.
• [13] Mesghouni K., Hammadi S. and Borne P. (1998): On modeling genetic algorithm for flexible job-shop scheduling problem. —Stud. Inform. Contr. J., Vol. 7, No. 1, pp. 37–47.
• [14] Mesghouni K. (1999): Application des algorithmes évolutionnistes dans les problemes d’optimisation en ordonnancement de la production.—Ph.D. Thesis, Lille 1 University, France.
• [15] Mesghouni K., Pesin P., Trentesaux D., Hammadi S., Tahon C. and Borne P. (1999): Hybrid approach to decision making for job-shop scheduling.—Prod. Plann. Contr. J., Vol. 10, No. 7, pp. 690–706.
• [16] Michalewicz Z. (1992): Genetic Algorithms + Data Structures = Evolution Programs.—Berlin: Springer.
• [17] Portman C.M. (1996): Genetic algorithms and scheduling: A state of the art and some proposition. — Proc. Workshop Production Planning and Control, Mons, Belgium, pp. ixxiv.
• [18] Quagliarella D., Périaux J., Poloni C. andWinter G. (1998): Genetic Algorithms and Evolution Strategies in Engineering and Computer Sciences. — England: John Whiley.
• [19] Syswerda G. (1990): Schedule optimization using genetic algorithm, In: Handbook of Genetic Algorithm. — pp. 323–349, New York: Van Nostrand Reinhold.
• [20] Uckun S., Baghi S. and Kawamura K. (1993): Managing genetic search in job-shop scheduling. — IEEE Expert, Vol. 8, No. 5, pp. 15–24.