Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling
Dynamic job shop scheduling (JSS) problems with dynamic job arrivals have been studied extensively in the literature due to their applicability to real-world manufacturing systems, such as semiconductor manufacturing. In a dynamic JSS problem with dynamic job arrivals, jobs arrive on the shop floor unannounced that need to be processed by the machines on the shop floor. A job has a sequence of operations that can only processed on specific machines, and machines can only process one job at a time. Many effective genetic programming based hyper-heuristic (GP-HH) approaches have been proposed for dynamic JSS problems with dynamic job arrivals, where high quality dispatching rules are automatically evolved by GP to handle the dynamic JSS problem instances. However, research that focus on handling multiple dynamic events simultaneously are limited, such as both dynamic job arrivals and machine breakdowns. A machine breakdown event results in the affected machine being unable to process any jobs during the repair time. It is likely that machine breakdowns can significantly affect the effectiveness of the scheduling procedure unless they are explicitly accounted for. Therefore, this paper develops new machine breakdown terminals for a GP approach and evaluates their effectiveness for a dynamic JSS problem with both dynamic job arrivals and machine breakdowns. The results show that the GP approaches with the machine breakdown terminals do show improvements. The analysis shows that the machine breakdown terminals may indirectly contribute in the evolution of high quality rules, but occur infrequently in the output rules evolved by the machine breakdown GP approaches.
- 3.Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2010), pp. 257–264. ACM, New York (2010)Google Scholar
- 6.Nguyen, S., Mei, Y., Ma, H., Chen, A., Zhang, M.: Evolutionary scheduling and combinatorial optimisation: applications, challenges, and future directions. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2016), pp. 3053–3060 (2016)Google Scholar
- 7.Hunt, R., Johnston, M., Zhang, M.: Evolving “less-myopic" scheduling rules for dynamic job shop scheduling with genetic programming. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2014), pp. 927–934. ACM, New York (2014)Google Scholar
- 8.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)Google Scholar
- 10.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 CrossRefGoogle Scholar
- 15.Mei, Y., Zhang, M., Nguyen, S.: Feature selection in evolving job shop dispatching rules with genetic programming. In: Proceedings of the 2016 Conference on Genetic and Evolutionary Computation, pp. 365–372 (2016)Google Scholar