Abstract
This paper deals with a two-objective rescheduling problem in a job shop for alteration of due date. One objective of this problem is to minimize the total tardiness, and the other is to minimize the difference of schedule. A genetic algorithm is proposed, and a new selection operation is particularly introduced to obtain the Pareto optimal solutions in the problem. At every generation in the proposed method, two solutions are picked up as the parents. While one of them is picked up from the population, the other is picked up from the archive solution set. Then, two solutions are selected from these parents and four children generated by means of the crossover and the mutation operation. The candidates selected are not only solutions close to the Pareto-optimal front but also solutions with a smaller value of the total tardiness, because the initial solutions are around the solution in which the total tardiness is zero. For this purpose, the solution space is ranked on the basis of the archive solutions. It is confirmed from the computational result that the proposed method outperforms other methods.
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References
Vieira, G.E., Herrmann, J.W., Lin, E.: Rescheduling manufacturing systems: A framework of strategies, policies and methods. Journal of Scheduling 6, 39–62 (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison–Wesley, Reading (1989)
Lin, S., Goodman, E.D., Punch III, W.F.: A genetic algorithm approach to dynamic job shop scheduling problems. In: Proceedings of 7th International Conference on Genetic Algorithms, pp. 481–488 (1997)
Bierwirth, C., Mattfeld, D.C.: Production scheduling and rescheduling with genetic algorithms. IEEE Transactions on Evolutionary Computation 7, 1–17 (1999)
Branke, J., Mattfeld, D.C.: Anticipation in dynamic optimization: The scheduling case. In: Proceedings of Parallel Problem Solving from Nature VI, pp. 253–262 (2000)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2002)
Watatani, Y., Fujii, S.: A study on rescheduling policy in production system. In: JAPAN/USA Symposium on Flexible Automation, vol. 2, pp. 1147–1150 (1992)
Abumaizar, R.J., Svestka, J.A.: Rescheduling job shops under random disruptions. International Journal of Production Research 35, 2065–2082 (1997)
Iima, H., Nakase, R., Sannomiya, N.: Genetic algorithm approach to a multiobjective rescheduling problem in a job shop. In: Proceedings of 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, pp. 422–437 (2003)
Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of First International Conference on Genetic Algorithms, pp. 136–140 (1985)
Nakano, R.: Conventional genetic algorithm for job shop problems. In: Proceedings of Fourth International Conference on Genetic Algorithms, pp. 474–479 (1991)
Fang, H.L., Ross, P., Corne, D.: A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems. In: Proceedings of Fifth International Conference on Genetic Algorithms, pp. 375–382 (1993)
Gen, M., Cheng, R.: Genetic Algorithms & Engineering Design. John Wiley & Sons, Chichester (1997)
Ono, I., Yamamura, M., Kobayashi, S.: A genetic algorithm for job-shop scheduling problems using job-based order crossover. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 547–552 (1996)
Shi, G., Iima, H., Sannomiya, N.: A new encoding scheme for solving job shop problems by genetic algorithm. In: Proceedings of 35th IEEE Conference on Decision and Control, pp. 4395–4400 (1996)
Fonseca, C.C., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of 5th International Conference on Genetic Algorithms, pp. 416–423 (1993)
Lawrence, S.: Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Graduate School of Industrial Administration, Carnegie-Mellon University (1984)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the performance of the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab, Swiss Federal Institute of Technology (2001)
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Iima, H. (2005). Proposition of Selection Operation in a Genetic Algorithm for a Job Shop Rescheduling Problem. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_50
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DOI: https://doi.org/10.1007/978-3-540-31880-4_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24983-2
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