Advertisement

Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling

  • John ParkEmail author
  • Yi Mei
  • Su Nguyen
  • Gang Chen
  • Mengjie ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10781)

Abstract

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.

References

  1. 1.
    Potts, C.N., Strusevich, V.A.: Fifty years of scheduling: a survey of milestones. J. Oper. Res. Soc. 60(1), S41–S68 (2009)CrossRefzbMATHGoogle Scholar
  2. 2.
    Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12(4), 417–431 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 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
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 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. 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. 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
  9. 9.
    Holthaus, O.: Scheduling in job shops with machine breakdowns: an experimental study. Comput. Ind. Eng. 36(1), 137–162 (1999)CrossRefGoogle Scholar
  10. 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
  11. 11.
    Pinedo, M., Hadavi, K.: Scheduling: theory, algorithms and systems development. In: Gaul, W., Bachem, A., Habenicht, W., Runge, W., Stahl, W.W. (eds.) ORP 1991. Operations Research Proceedings 1991, vol. 1991. Springer, Heidelberg (1992).  https://doi.org/10.1007/978-3-642-46773-8_5 Google Scholar
  12. 12.
    Geiger, C.D., Uzsoy, R., Aytuğ, H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Sched. 9(1), 7–34 (2006)CrossRefzbMATHGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  15. 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
  16. 16.
    Vepsalainen, A.P.J., Morton, T.E.: Priority rules for job shops with weighted tardiness costs. Manag. Sci. 33(8), 1035–1047 (1987)CrossRefGoogle Scholar
  17. 17.
    Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Evol. Comput. 23(3), 343–367 (2015)CrossRefGoogle Scholar
  18. 18.
    Hart, E., Sim, K.: A hyper-heuristic ensemble method for static job-shop scheduling. Evol. Comput. 24(4), 609–635 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Evolutionary Computation Research GroupVictoria University of WellingtonWellingtonNew Zealand
  2. 2.La Trobe UniversityMelbourneAustralia

Personalised recommendations