Skip to main content
Springer Nature Link
Log in

Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

Included in the following conference series:

Abstract

Genetic Programming (GP) has achieved success in evolving dispatching rules for job shop scheduling problems, particularly in dynamic environments. However, there is still great potential to improve the performance of GP. One challenge that is yet to be addressed is the huge search space. In this paper, we propose a simple yet effective approach to improve the effectiveness and efficiency of GP. The new approach is based on a newly defined time-invariance property of dispatching rules, which is derived from the idea of translational invariance from machine learning. Then, we develop a new terminal selection scheme to guarantee the time-invariance throughout the GP process. The experimental studies show that by considering the time-invariance, GP can achieve much better rules in a much shorter time.

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. Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer Science & Business Media, New York (2012)

    Book  MATH  Google Scholar 

  2. Ceberio, J., Irurozki, E., Mendiburu, A., Lozano, J.A.: A distance-based ranking model estimation of distribution algorithm for the flowshop scheduling problem. IEEE Trans. Evol. Comput. 18(2), 286–300 (2014)

    Article  Google Scholar 

  3. Marichelvam, M.K., Prabaharan, T., Yang, X.S.: A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans. Evol. Comput. 18(2), 301–305 (2014)

    Article  Google Scholar 

  4. Xiong, J., Liu, J., Chen, Y., Abbass, H.A.: A knowledge-based evolutionary multiobjective approach for stochastic extended resource investment project scheduling problems. IEEE Trans. Evol. Comput. 18(5), 742–763 (2014)

    Article  Google Scholar 

  5. Branke, J., Nguyen, S., Pickardt, C., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2016)

    Article  Google Scholar 

  6. Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 20(1), 27–45 (1982)

    Article  Google Scholar 

  7. Sels, V., Gheysen, N., Vanhoucke, M.: A comparison of priority rules for the job shop scheduling problem under different flow time-and tardiness-related objective functions. Int. J. Prod. Res. 50(15), 4255–4270 (2012)

    Article  Google Scholar 

  8. Holthaus, O., Rajendran, C.: Efficient dispatching rules for scheduling in a job shop. Int. J. Prod. Econ. 48(1), 87–105 (1997)

    Article  Google Scholar 

  9. Jayamohan, M., Rajendran, C.: New dispatching rules for shop scheduling: a step forward. Int. J. Prod. Res. 38(3), 563–586 (2000)

    Article  MATH  Google Scholar 

  10. Rajendran, C., Holthaus, O.: A comparative study of dispatching rules in dynamic flowshops and jobshops. Eur. J. Oper. Res. 116(1), 156–170 (1999)

    Article  MATH  Google Scholar 

  11. 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, pp. 257–264. ACM (2010)

    Google Scholar 

  12. Jakobović, D., Budin, L.: Dynamic scheduling with genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 73–84. Springer, Heidelberg (2006). doi:10.1007/11729976_7

    Chapter  Google Scholar 

  13. Nguyen, S., Zhang, M., Johnston, M., Tan, K.: 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)

    Article  Google Scholar 

  14. Ho, N., Tay, J.: Evolving dispatching rules for solving the flexible job-shop problem. In: IEEE Congress on Evolutionary Computation, vol. 3, pp. 2848–2855. IEEE (2005)

    Google Scholar 

  15. Pickardt, C., Hildebrandt, T., Branke, J., Heger, J., Scholz-Reiter, B.: Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. Int. J. Prod. Econ. 145(1), 67–77 (2013)

    Article  Google Scholar 

  16. Hunt, R., Johnston, M., Zhang, M.: Evolving less-myopic scheduling rules for dynamic job shop scheduling with genetic programming. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 927–934. ACM (2014)

    Google Scholar 

  17. Mei, Y., Zhang, M., Nyugen, S.: Feature selection in evolving job shop dispatching rules with genetic programming. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 365–372. ACM (2016)

    Google Scholar 

  18. Riley, M., Mei, Y., Zhang, M.: Feature selection in evolving job shop dispatching rules with genetic programming. In: IEEE Congress on Evolutionary Computation. IEEE (2016)

    Google Scholar 

  19. Durasević, M., Jakobović, D., Knežević, K.: Adaptive scheduling on unrelated machines with genetic programming. Appl. Soft Comput. 48, 419–430 (2016)

    Article  Google Scholar 

  20. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  22. Nguyen, S., Zhang, M., Tan, K.C.: Surrogate-assisted genetic programming with simplified models for automated design of dispatching rules. IEEE Trans. Cybern. 1–15 (2016). doi:10.1109/TCYB.2016.2562674

  23. Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Dynamic multi-objective job shop scheduling: a genetic programming approach. In: Uyar, A.S., Ozcan, E., Urquhart, N. (eds.) Automated Scheduling and Planning. SCI, vol. 505, pp. 251–282. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39304-4_10

    Chapter  Google Scholar 

  24. Luke, S., et al.: A Java-based evolutionary computation research system. https://cs.gmu.edu/~eclab/projects/ecj/

Download references

Author information

Authors and Affiliations

  1. Victoria University of Wellington, Wellington, New Zealand

    Yi Mei, Su Nguyen & Mengjie Zhang

  2. Department of Business, Hoa Sen University, Ho Chi Minh City, Vietnam

    Su Nguyen

Authors
  1. Yi Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Su Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Mei .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mei, Y., Nguyen, S., Zhang, M. (2017). Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55696-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55695-6

  • Online ISBN: 978-3-319-55696-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation