Evolutionary Multitask Optimisation for Dynamic Job Shop Scheduling Using Niched Genetic Programming

  • John ParkEmail author
  • Yi Mei
  • Su Nguyen
  • Gang Chen
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)


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.


  1. 1.
    Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 4th edn. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Potts, C.N., Strusevich, V.A.: Fifty years of scheduling: a survey of milestones. J. Oper. Res. Soc. 60(1), S41–S68 (2009)CrossRefGoogle Scholar
  5. 5.
    Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12(4), 417–431 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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). Scholar
  9. 9.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  10. 10.
    Ong, Y.S., Gupta, A.: Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn. Comput. 8(2), 125–142 (2016)CrossRefGoogle Scholar
  11. 11.
    Gupta, A., Ong, Y.S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016)CrossRefGoogle Scholar
  12. 12.
    Holthaus, O.: Scheduling in job shops with machine breakdowns: an experimental study. Comput. Ind. Eng. 36(1), 137–162 (1999)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Evol. Comput. 23(3), 343–367 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • John Park
    • 1
    Email author
  • Yi Mei
    • 1
  • Su Nguyen
    • 1
    • 2
  • Gang Chen
    • 1
  • Mengjie Zhang
    • 1
  1. 1.Evolutionary Computation Research GroupVictoria University of WellingtonWellingtonNew Zealand
  2. 2.La Trobe UniversityMelbourneAustralia

Personalised recommendations