Abstract
The Job Shop Scheduling Problem with Sequence Dependent Setup Times (SDJSS) is an extension of the Job Shop Scheduling Problem (JSS) that has interested to researchers during the last years. In this paper we confront the SDJSS problem by means of a memetic algorithm. We study two schedule generation schemas that are extensions of the well known G&T algorithm for the JSS. We report results from an experimental study showing that the proposed approaches produce similar results and that both of them are more efficient than other genetic algorithm proposed in the literature.
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Artigues, C., Lopez, P., P.D., A.: Schedule generation schemes for the job shop problem with sequence-dependent setup times: Dominance properties and computational analysis. Annals of Operational Research 138, 21–52 (2005)
Bierwirth, C.: A Generalized Permutation Approach to Jobshop Scheduling with Genetic Algorithms. OR Spectrum 17, 87–92 (1995)
Brucker, P., Jurisch, B., Sievers, B.: A branch and bound algorithm for the job-shop scheduling problem. Discrete Applied Mathematics 49, 107–127 (1994)
Brucker, P., Thiele, O.: A branch and bound method for the general-job shop problem with sequence-dependent setup times. Operations Research Spektrum 18, 145–161 (1996)
Brucker, P.: Scheduling Algorithm, 4th edn. Springer, Heidelberg (2004)
Carlier, J., Pinson, E.: Adjustment of heads and tails for the job-shop problem. European Journal of Operational Research 78, 146–161 (1994)
Cheung, W., Zhou, H.: Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times. Annals of Operational Research 107, 65–81 (2001)
Dell Amico, M., Trubian, M.: Applying Tabu Search to the Job-shop Scheduling Problem. Annals of Operational Research 41, 231–252 (1993)
Giffler, B., Thomson, G.L.: Algorithms for Solving Production Scheduling Problems. Operations Reseach 8, 487–503 (1960)
González, M.A., Sierra, M.R., Vela, C.R., Varela, R.: Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules. LNCS, Springer, Heidelberg (to appear, 2006)
González, M.A., Vela, C.R., Puente, J., Sierra, M.R., Varela, R.: Memetic Algorithms for the Job Shop Scheduling Problem with Sequence Dependent Setup Times. In: Proceedings of ECAI Workshop on Evolutionary Computation (to appear, 2006)
Mattfeld, D.C.: Evolutionary Search and the Job Shop. Investigations on Genetic Algorithms for Production Scheduling, November 1995. Springer, Heidelberg (1995)
Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Management Science 42, 797–813 (1996)
Ovacik, I.M., Uzsoy, R.: Exploiting shop floors status information to schedule complex jobs. Operations Research Letters 14, 251–256 (1993)
Taillard, E.D.: Parallel Taboo Search Techniques for the Job Shop Scheduling Problem. ORSA Journal of Computing 6, 108–117 (1993)
Varela, R., Vela, C.R., Puente, J., Gmez, A.: A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Operational Research 145, 57–71 (2003)
Varela, R., Serrano, D., Sierra, M.: New Codification Schemas for Scheduling with Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 11–20. Springer, Heidelberg (2005)
Zoghby, J., Barnes, J.W., Hasenbein, J.J.: Modeling the re-entrant job shop scheduling problem with setup for metaheuristic searches. European Journal of Operational Research 167, 336–348 (2005)
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González, M.A., Vela, C.R., Sierra, M., González, I., Varela, R. (2006). Comparing Schedule Generation Schemes in Memetic Algorithms for the Job Shop Scheduling Problem with Sequence Dependent Setup Times. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_44
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DOI: https://doi.org/10.1007/11925231_44
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