Advertisement

Dynamic Multi-objective Job Shop Scheduling: A Genetic Programming Approach

  • Su NguyenEmail author
  • Mengjie Zhang
  • Mark Johnston
  • Kay Chen Tan
Part of the Studies in Computational Intelligence book series (SCI, volume 505)

Abstract

Handling multiple conflicting objectives in dynamic job shop scheduling is challenging because many aspects of the problem need to be considered when designing dispatching rules. A multi-objective genetic programming based hyperheuristic (MO-GPHH) method is investigated here to facilitate the designing task. The goal of this method is to evolve a Pareto front of non-dominated dispatching rules which can be used to support the decision makers by providing them with potential trade-offs among different objectives. The experimental results under different shop conditions suggest that the evolved Pareto front contains very effective rules. Some extensive analyses are also presented to help confirm the quality of the evolved rules. The Pareto front obtained can cover a much wider ranges of rules as compared to a large number of dispatching rules reported in the literature.Moreover, it is also shown that the evolved rules are robust across different shop conditions.

Keywords

Pareto Front Short Processing Time Bottleneck Machine Shop Condition Genetic Programming Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Atlan, L., Bonnet, J., Naillon, M.: Learning distributed reactive strategies by genetic programming for the general job shop problem. In: Proceedings of the 7th Annual Florida Artificial Intelligence Research Symposium (1994)Google Scholar
  2. 2.
    Baker, K.R.: Sequencing rules and due-date assignments in a job shop. Management Science 30, 1093–1104 (1984)CrossRefGoogle Scholar
  3. 3.
    Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)zbMATHCrossRefGoogle Scholar
  4. 4.
    Bhowan, U., Johnston, M., Zhang, M., Yao, X.: Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE Transactions on Evolutionary Computation (2012), doi:10.1109/TEVC.2012.2199119Google Scholar
  5. 5.
    Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring hyper-heuristic methodologies with genetic programming. Artificial Evolution 1, 177–201 (2009)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Dimopoulos, C., Zalzala, A.M.S.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software 32(6), 489–498 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Geiger, C.D., Uzsoy, R.: Learning effective dispatching rules for batch processor scheduling. International Journal of Production Research 46, 1431–1454 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    Geiger, C.D., Uzsoy, R., Aytug, H.: Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach. Journal of Heuristics 9(1), 7–34 (2006), doi:http://dx.doi.org/10.1007/s10951-006-5591-8 Google Scholar
  10. 10.
    Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM, New York (2010)CrossRefGoogle Scholar
  11. 11.
    Holthaus, O., Rajendran, C.: Efficient jobshop dispatching rules: Further developments. Production Planning & Control 11(2), 171–178 (2000)CrossRefGoogle Scholar
  12. 12.
    Ingimundardottir, H., Runarsson, T.P.: Supervised learning linear priority dispatch rules for job-shop scheduling. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 263–277. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Jakobović, D., Jelenković, L., Budin, L.: Genetic programming heuristics for multiple machine scheduling. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 321–330. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Jayamohan, M.S., Rajendran, C.: New dispatching rules for shop scheduling: a step forward. International Journal of Production Research 38, 563–586 (2000)zbMATHCrossRefGoogle Scholar
  16. 16.
    Jones, A., Rabelo, L.C.: Survey of job shop scheduling techniques. Tech. rep., NISTIR, National Institute of Standards and Technology, Gaithersburg, US (1998)Google Scholar
  17. 17.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)Google Scholar
  18. 18.
    Li, X., Olafsson, S.: Discovering dispatching rules using data mining. Journal of Scheduling 8, 515–527 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Miyashita, K.: Job-shop scheduling with GP. In: GECCO 2000: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 505–512 (2000)Google Scholar
  20. 20.
    Montgomery, D.C.: Design and Analysis of Experiments. John Wiley & Sons (2001)Google Scholar
  21. 21.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems. In: CEC 2012: IEEE Congress on Evolutionary Computation, pp. 3332–3339 (2012)Google Scholar
  22. 22.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Evolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) EuroGP 2012. LNCS, vol. 7244, pp. 121–133. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Nie, L., Shao, X., Gao, L., Li, W.: Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. The International Journal of Advanced Manufacturing Technology 50, 729–747 (2010)CrossRefGoogle Scholar
  24. 24.
    Panwalkar, S.S., Iskander, W.: A survey of scheduling rules. Operations Research 25, 45–61 (1977)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 3rd edn. Springer (2008)Google Scholar
  26. 26.
    Rafter, J.A., Abell, M.L., Braselton, J.P.: Multiple comparison methods for means. SIAM Review 44(2), 259–278 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Rajendran, C., Holthaus, O.: A comparative study of dispatching rules in dynamic flowshops and jobshops. European Journal of Operational Research 116(1), 156–170 (1999)zbMATHCrossRefGoogle Scholar
  28. 28.
    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. International Journal of Production Research (2011)Google Scholar
  29. 29.
    Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computer and Industrial Engineering 54, 453–473 (2008)CrossRefGoogle Scholar
  30. 30.
    Vepsalainen, A.P.J., Morton, T.E.: Priority rules for job shops with weighted tardiness costs. Management Science 33, 1035–1047 (1987)CrossRefGoogle Scholar
  31. 31.
    Wang, Z., Tang, K., Yao, X.: Multi-objective approaches to optimal testing resource allocation in modular software systems. IEEE Transactions on Reliability 59(3), 563–575 (2010)CrossRefGoogle Scholar
  32. 32.
    Yin, W.J., Liu, M., Wu, C.: Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: CEC 2003: IEEE Congress on Evolutionary Computation, pp. 1050–1055 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Su Nguyen
    • 1
    Email author
  • Mengjie Zhang
    • 1
  • Mark Johnston
    • 1
  • Kay Chen Tan
    • 2
  1. 1.Victoria University of WellingtonWellingtonNew Zealand
  2. 2.National University of SingaporeSingaporeSingapore

Personalised recommendations