New data oriented genetic operators for solving Flowshop problems having tardiness criteria

Abdel Halim Mahdi 1 Gülgün Alpan 1 Marie-Claude Portmann 1
1 MACSI - Industrial system modeling, analysis and operation
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Genetic algorithms are used in scheduling leading to efficient heuristic methods for large sized problems. The efficiency of a GA based heuristic is closely related to the quality of the used GA scheme and the GA operators. The main focus of this paper is the use of new data oriented operators. The data in question could be, for instance, the maximum processing times among the jobs, the due dates of the tasks and so forth. The crossover or mutation is then done using this data. In this paper we consider the permutation flowshop problem. Minimization of the total weighted tardiness is taken as the objective function. We perform a series of experiments for m=10 machines and n=20 jobs. The machines are not identical and there may be bottleneck machines in the system. We compare our results with the results obtained from the classical GA operators. Our preliminary results prove to be encouraging in terms of the convergence speed and the total weighted tardiness.
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Communication dans un congrès
9th Industrial Engineering Research Conference 2000 - IERC'2000, 2000, Cleveland, USA, 7 p, 2000
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Soumis le : mardi 26 septembre 2006 - 08:53:07
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00099341, version 1

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Abdel Halim Mahdi, Gülgün Alpan, Marie-Claude Portmann. New data oriented genetic operators for solving Flowshop problems having tardiness criteria. 9th Industrial Engineering Research Conference 2000 - IERC'2000, 2000, Cleveland, USA, 7 p, 2000. 〈inria-00099341〉

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