Skip to main content
Springer Nature Link
Log in

Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling

  • Conference paper
  • First Online:
Parallel Problem Solving from Nature – PPSN XVII (PPSN 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13399))

Included in the following conference series:

Abstract

Dynamic flexible job shop scheduling (DFJSS) is a critical and challenging problem in production scheduling such as order picking in the warehouse. Given a set of machines and a number of jobs with a sequence of operations, DFJSS aims to generate schedules for completing jobs to minimise total costs while reacting effectively to dynamic changes. Genetic programming, as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DFJSS automatically. A scheduling heuristic in DFJSS includes a routing rule for machine assignment and a sequencing rule for operation sequencing. However, existing studies assume that the routing and sequencing are equally important, which may not be true in real-world applications. This paper aims to propose an importance-aware GP algorithm for automated scheduling heuristics learning in DFJSS. Specifically, we first design a rule importance measure based on the fitness improvement achieved by the routing rule and the sequencing rule across generations. Then, we develop an adaptive resource allocation strategy to give more resources for learning the more important rules. The results show that the proposed importance-aware GP algorithm can learn significantly better scheduling heuristics than the compared algorithms. The effectiveness of the proposed algorithm is realised by the proposed strategies for detecting rule importance and allocating resources. Particularly, the routing rules play a more important role than the sequencing rules in the examined DFJSS scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nie, L., Gao, L., Li, P., Li, X.: A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. J. Intell. Manuf. 24(4), 763–774 (2013)

    Article  Google Scholar 

  2. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Guided subtree selection for genetic operators in genetic programming for dynamic flexible job shop scheduling. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds.) EuroGP 2020. LNCS, vol. 12101, pp. 262–278. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44094-7_17

    Chapter  Google Scholar 

  3. Zhang, F., Nguyen, S., Mei, Y., Zhang, M.: Genetic Programming for Production Scheduling. MLFMA, Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-4859-5

    Book  Google Scholar 

  4. Nguyen, S., Zhang, M., Johnston, M., Chen Tan, K.: Hybrid evolutionary computation methods for quay crane scheduling problems. Comput. Oper. Res. 40(8), 2083–2093 (2013)

    Article  Google Scholar 

  5. Hart, E., Ross, P., Corne, D.: Evolutionary scheduling: a review. Genet. Program Evolvable Mach. 6(2), 191–220 (2005)

    Article  Google Scholar 

  6. Jaklinović, K., Durasević, M., Jakobović, D.: Designing dispatching rules with genetic programming for the unrelated machines environment with constraints. Exp. Syst. Appl. 172, 114548 (2021)

    Google Scholar 

  7. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Correlation coefficient-based recombinative guidance for genetic programming hyperheuristics in dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25(3), 552–566 (2021). https://doi.org/10.1109/TEVC.2021.3056143

    Article  Google Scholar 

  8. Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994)

    Article  Google Scholar 

  9. Burke, E.K., et al.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)

    Article  Google Scholar 

  10. Braune, R., Benda, F., Doerner, K.F., Hartl, R.F.: A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems. Int. J. Prod. Econ. 243, 108342 (2022)

    Article  Google Scholar 

  11. Pillay, N., Qu, R.: Hyper-Heuristics: Theory and Applications. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96514-7

    Book  Google Scholar 

  12. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Trans. Cybern. 52(8), 8142–8156 (2022). https://doi.org/10.1109/TCYB.2021.3050141

    Article  Google Scholar 

  13. Zhang, F., Mei, Y., Nguyen, S., Zhang, M., Tan, K.C.: Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25(4), 651–665 (2021)

    Article  Google Scholar 

  14. Zhang, F., Mei, Y., Nguyen, S., Tan, K.C., Zhang, M.: Multitask genetic programming-based generative hyper-heuristics: a case study in dynamic scheduling. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3065340

  15. Shen, X., Guo, Y., Li, A.: Cooperative coevolution with an improved resource allocation for large-scale multi-objective software project scheduling. Appl. Soft Comput. 88, 106059 (2020)

    Article  Google Scholar 

  16. Ren, Z., Liang, Y., Zhang, A., Yang, Y., Feng, Z., Wang, L.: Boosting cooperative coevolution for large scale optimization with a fine-grained computation resource allocation strategy. IEEE Trans. Cybern. 49(12), 4180–4193 (2018)

    Article  Google Scholar 

  17. Yang, M., et al.: Efficient resource allocation in cooperative co-evolution for large-scale global optimization. IEEE Trans. Evol. Comput. 21(4), 493–505 (2017). https://doi.org/10.1109/TEVC.2016.2627581

    Article  Google Scholar 

  18. Jia, Y.-H., Mei, Y., Zhang, M.: Contribution-based cooperative co-evolution for nonseparable large-scale problems with overlapping subcomponents. IEEE Trans. Cybern. 52(6), 4246–4259 (2020). https://doi.org/10.1109/TCYB.2020.3025577

    Article  Google Scholar 

  19. Zhang, X.-Y., Gong, Y.-J., Lin, Y., Zhang, J., Kwong, S., Zhang, J.: Dynamic cooperative coevolution for large scale optimization. IEEE Trans. Evol. Comput. 23(6), 935–948 (2019)

    Article  Google Scholar 

  20. Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45(4), 369–375 (1990)

    Article  MathSciNet  Google Scholar 

  21. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 107–108. ACM (2020)

    Google Scholar 

  22. Durasevic, M., Jakobovic, D.: A survey of dispatching rules for the dynamic unrelated machines environment. Exp. Syst. Appl. 113, 555–569 (2018)

    Article  Google Scholar 

  23. Hart, E., Sim, K.: A hyper-heuristic ensemble method for static job-shop scheduling. Evol. Comput. 24(4), 609–635 (2016)

    Article  Google Scholar 

  24. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Genetic programming with adaptive search based on the frequency of features for dynamic flexible job shop scheduling. In: Paquete, L., Zarges, C. (eds.) EvoCOP 2020. LNCS, vol. 12102, pp. 214–230. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43680-3_14

    Chapter  Google Scholar 

  25. 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., García-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

    Chapter  Google Scholar 

  26. Zhang, F., Mei, Y., Zhang, M.: A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 347–355. ACM (2019)

    Google Scholar 

  27. Hildebrandt, T., Heger, J., Reiter, B.S.: Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM (2010)

    Google Scholar 

  28. Zhang, F., Mei, Y., Nguyen, S., Tan, K.C., Zhang, M.: Instance rotation based surrogate in genetic programming with brood recombination for dynamic job shop scheduling. IEEE Trans. Evol. Comput. (2022). https://doi.org/10.1109/TEVC.2022.3180693

  29. Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Evol. Comput. 23(3), 343–367 (2015)

    Article  Google Scholar 

  30. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job-shop scheduling. IEEE Trans. Cybern. 51(4), 1797–1811 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, PO BOX 600, Wellington, 6140, New Zealand

    Fangfang Zhang, Yi Mei & Mengjie Zhang

  2. Centre for Data Analytics and Cognition, La Trobe University, Bundoora, Australia

    Su Nguyen

Authors
  1. Fangfang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Su Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fangfang Zhang .

Editor information

Editors and Affiliations

  1. TU Dortmund, Dortmund, Germany

    Günter Rudolph

  2. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  3. Shinshu University, Nagano, Japan

    Hernán Aguirre

  4. Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  5. University of Stirling, Stirling, UK

    Gabriela Ochoa

  6. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, F., Mei, Y., Nguyen, S., Zhang, M. (2022). Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13399. Springer, Cham. https://doi.org/10.1007/978-3-031-14721-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-14721-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14720-3

  • Online ISBN: 978-3-031-14721-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation