Abstract
The resolution of workshop problems such as the Flow Shop or the Job Shop has a great importance in many industrial areas. The criteria to optimize are generally the minimization of the makespan or the tardiness. However, few are the resolution approaches that take into account those different criteria simultaneously. This paper presents an approach based on hybrid genetic algorithms adapted to the multicriteria case. Several strategies of selection and diversity maintaining are presented. Their performances are evaluated and compared using different benchmarks. A parallel model is also proposed and implemented for the hybrid metaheuristic. It allows to increase the population size and the number of generations, and then leads to better results.
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References
Baker, J.E. Adaptive selection methods for Genetic Algorithms. Proceeding of international conference on Genetic Algorithms and their application, Page 101, 1985.
Bentley, P.J., Wakefield, J.P. Find an acceptable Pareto-optimal solutions using multiobjective Genetic Algorithms. Springer Verlag, London, page 231, June 1997.
Brah, S.B., Hunsucker, J.L. Branch & Bound algorithm for the flow-shop with multiple processors. European Journal of Operational Research, Vol 51, page 88, 1991.
Fonseca, C. M., Fleming, P.J. Multiobjective genetic algorithms made easy: selection, sharing and mating restrictions. In IEEE Int. Conf On Genetic Algorithms in Engineering System: Innovations and Applications, Page 45, Sheffield, UK, 1995.
Fujita, K., Hirokawa, N., Akagi, S., Kimatura, S., Yokohata, H. Multi-objective optimal design of automotive engine using genetic algorithm. In Proceedings of DETC’98-ASME Design Engineering Technical Conferences, page 11, 1998.
Gonzalez, T., Sahni, S. Flowshop and Job-shop Schedules: Complexity and Approximation. Operational Research, Vol 26, N°1, page 36, 1978.
Gupta, N.D. An improved Combinatorial Algorithm For The Flowshop-Scheduling Problem. Operational Research, Vol 19, page 1753, 1969.
Hajela, P., Lin, C.Y. Genetic search strategies in multicriterion optimal design. Structural Optimisation, (4) Page 99, 1992.
Heller, J. Some Numerical Experiments For a MxJ Flow Shop And Its Decision Theoretical Aspects. Operational Research, Vol 8, page 178, 1960.
Murata, T., Ishibuchi, H. A Multi-objectives Genetic Local Search Algorithm and Its Application Flow-shop Scheduling. IEEE Transaction System. Vol 28, N°3, pp 392, 1998.
Nowicki, E. The permutation Flow shop with buffers: A tabu search approach. European Journal of Operational Research, Vol 116, page 205, 1999.
Rajendran, C., Chaudhuri, D. An efficient heuristic approach to the scheduling of jobs in flow-shop. European Journal Of Operational Research, Vol 61, page 318, 1991.
Simon French, F., Phil, D. Sequencing and scheduling: An introduction to the mathematic of the Job-Shop. Department of Decision Theory, University of Manchester. John Wiley & Sons Edition, 1982.
Srinivas, N., Deb, K. Multiobjective optimisation using non-dominated sorting in genetic algorithms. Evolutionary Computation 2(8), page 221, 1995.
Taillard, E. Benchmarks for basic scheduling problems. European Journal of Operational Research, Vol 64, Page 278, 1993.
Talbi, E-G. Métaheuristiques pour l'optimisation combinatoire multi-objectifs: Etat de l’art. Rapport interne, Université de sciences et Technologies de Lille, France, Jun 1999.
Van Veldhuizen, D.A., Lamount, G.B. Multiobjective Evolutionary Algorithm Research: A History and Analysis.Technical Report98–03, Department of Electrical and Computer Engineering, Air Force Institute of Technology, USA, Dec 1998.
Zitzler, E. Evolutionary Algorithms for Multiobjective Optimization: Methods and Application. Dissertation submitted to the Swiss Federal Institute of Technology Zurich for a degree of Doctor of technical science, Nov 1999.
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Talbi, EG., Rahoual, M., Mabed, M.H., Dhaenens, C. (2001). A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_29
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DOI: https://doi.org/10.1007/3-540-44719-9_29
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