Abstract
Dynamic job shop scheduling (DJSS) problems are combinatorial optimisation problems where dynamic events occur during processing that prevents scheduling algorithms from being able to predict the optimal solutions in advance. DJSS problems have been studied extensively due to the difficulty of the problem and their applicability to real-world scenarios. This paper deals with a DJSS problem with dynamic job arrivals and machine breakdowns. A standard genetic programming (GP) approach that evolves dispatching rules, which is effective for DJSS problems with dynamic job arrivals, have difficulty generalising over problem instances with different machine breakdown scenarios. This paper proposes a niched GP approach that incorporates multitasking to simultaneously evolve multiple rules that can effectively cope with different machine breakdown scenarios. The results show that the niched GP approach can evolve rules for the different machine breakdown scenarios faster than the combined computation times of the benchmark GP approach and significantly outperform the benchmark GP’s evolved rules. The analysis shows that the specialist rules effective for DJSS problem instances with zero machine breakdown have different behaviours to the rules effective for DJSS problem instances with machine breakdown and the generalist rules, but there is also large variance in the behaviours of the zero machine breakdown specialist rules.
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Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 4th edn. Springer, Heidelberg (2012)
Nguyen, S., Mei, Y., Zhang, M.: Genetic programming for production scheduling: a survey with a unified framework. Complex Intell. Syst. 3(1), 41–66 (2017)
Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2016)
Potts, C.N., Strusevich, V.A.: Fifty years of scheduling: a survey of milestones. J. Oper. Res. Soc. 60(1), S41–S68 (2009)
Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12(4), 417–431 (2009)
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)
Yin, W.J., Liu, M., Wu, C.: Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2003), pp. 1050–1055 (2003)
Park, J., Mei, Y., Nguyen, S., Chen, G., Zhang, M.: Investigating the generality of genetic programming based hyper-heuristic approach to dynamic job shop scheduling with machine breakdown. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS (LNAI), vol. 10142, pp. 301–313. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51691-2_26
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Ong, Y.S., Gupta, A.: Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn. Comput. 8(2), 125–142 (2016)
Gupta, A., Ong, Y.S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016)
Holthaus, O.: Scheduling in job shops with machine breakdowns: an experimental study. Comput. Ind. Eng. 36(1), 137–162 (1999)
Mei, Y., Nguyen, S., Xue, B., Zhang, M.: An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming. IEEE Trans. Emerg. Top. Comput. Intell. 1(5), 339–353 (2017)
Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Evol. Comput. 23(3), 343–367 (2015)
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Park, J., Mei, Y., Nguyen, S., Chen, G., Zhang, M. (2018). Evolutionary Multitask Optimisation for Dynamic Job Shop Scheduling Using Niched Genetic Programming. 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_66
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