Abstract
Genetic programming (GP) has been widely used for automatically evolving priority rules for solving job shop scheduling problems. However, one of the main drawbacks of GP is the intensive computation time. This paper aims at investigating appropriate surrogates for GP to reduce its computation time without sacrificing its performance in solving dynamic flexible job shop scheduling (DFJSS) problems. Firstly, adaptive surrogate strategy with dynamic fidelities of simulation models are proposed. Secondly, we come up with generation-range-based surrogate strategy in which homogeneous (heterogeneous) surrogates are used in same (different) ranges of generations. The results show that these two surrogate strategies with GP are efficient. The computation time are reduced by 22.9% to 27.2% and 32.6% to 36.0%, respectively. The test performance shows that the proposed approaches can obtain rules with at least the similar quality to the rules obtained by the GP approach without using surrogates. Moreover, GP with adaptive surrogates achieves significantly better performance in one out of six scenarios. This paper confirms the potential of using surrogates to solve DFJSS problems.
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References
Bhattacharya, M.: Reduced computation for evolutionary optimization in noisy environment. In: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 2117–2122. ACM (2008)
Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 20(1), 27–45 (1982)
Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45(4), 369–375 (1990)
Haupt, R.: A survey of priority rule-based scheduling. Oper.-Res.-Spektrum 11(1), 3–16 (1989)
Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM (2010)
Mei, Y., Nguyen, S., Zhang, M.: Evolving time-invariant dispatching rules in job shop scheduling with genetic programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., GarcÃa-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 147–163. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55696-3_10
Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Trans. Evol. Comput. 17(5), 621–639 (2013)
Ratle, A.: Accelerating the convergence of evolutionary algorithms by fitness landscape approximation. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 87–96. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056852
Sels, V., Gheysen, N., Vanhoucke, M.: A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions. Int. J. Prod. Res. 50(15), 4255–4270 (2012)
Yska, D., Mei, Y., Zhang, M.: Genetic programming hyper-heuristic with cooperative coevolution for dynamic flexible job shop scheduling. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., GarcÃa-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 306–321. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77553-1_19
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Zhang, F., Mei, Y., Zhang, M. (2018). Surrogate-Assisted Genetic Programming for Dynamic Flexible Job Shop Scheduling. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_69
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DOI: https://doi.org/10.1007/978-3-030-03991-2_69
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