Abstract
We consider a job shop problem with uncertain durations and flexible due dates and introduce a multiobjective model based on lexicographical minimisation. To solve the resulting problem, a genetic algorithm and a decoding algorithm to generate possibly active schedules are considered. The multiobjective approach is tested on several problem instances, illustrating the potential of the proposed method.
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Keywords
- Fuzzy Number
- Conventional Genetic Algorithm
- Fuzzy Processing Time
- Multiobjective Approach
- Uncertain Duration
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References
Brucker, P., Knust, S.: Complex Scheduling. Springer, Heidelberg (2006)
Dubois, D., Fargier, H., Fortemps, P.: Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research 147, 231–252 (2003)
Słowiński, R., Hapke, M. (eds.): Scheduling Under Fuzziness. Studies in Fuzziness and Soft Computing, vol. 37. Physica-Verlag, Heidelberg, New York (2000)
Fortemps, P.: Jobshop scheduling with imprecise durations: a fuzzy approach. IEEE Transactions of Fuzzy Systems 7, 557–569 (1997)
Sakawa, M., Kubota, R.: Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. European Journal of Operational Research 120, 393–407 (2000)
Fayad, C., Petrovic, S.: A fuzzy genetic algorithm for real-world job-shop scheduling. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 524–533. Springer, Heidelberg (2005)
González Rodríguez, I., Puente, J., Vela, C.R., Varela, R.: Semantics of schedules for the fuzzy job shop problem. IEEE Transactions on Systems, Man and Cybernetics, Part A (accepted for publication, 2007)
Liu, B., Liu, Y.K.: Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems 10, 445–450 (2002)
Celano, G., Costa, A., Fichera, S.: An evolutionary algorithm for pure fuzzy flowshop scheduling problems. Fuzziness and Knowledge-Based Systems 11, 655–669 (2003)
Bierwirth, C.: A generalized permutation approach to jobshop scheduling with genetic algorithms. OR Spectrum 17, 87–92 (1995)
González Rodríguez, I., Vela, C.R., Puente, J.: A memetic approach to fuzzy job shop based on expectation model. In: Proceedings of FUZZ-IEEE 2007 (2007)
Mattfeld, D.C.: Evolutionary Search and the Job Shop Investigations on Genetic Algorithms for Production Scheduling. Springer, Heidelberg (1995)
Varela, R., Vela, C.R., Puente, J., Gómez, 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.M., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 11–20. Springer, Heidelberg (2005)
Giffler, B., Thomson, G.L.: Algorithms for solving production scheduling problems. Operations Research 8, 487–503 (1960)
González Rodríguez, I., Vela, C.R., Puente, J.: Study of objective functions in fuzzy job-shop problem. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 360–369. Springer, Heidelberg (2006)
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González-Rodríguez, I., Puente, J., Vela, C.R. (2007). A Multiobjective Approach to Fuzzy Job Shop Problem Using Genetic Algorithms. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_9
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DOI: https://doi.org/10.1007/978-3-540-75271-4_9
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