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
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer Science & Business Media, New York (2012)
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)
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)
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)
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)
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)
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)
Holthaus, O., Rajendran, C.: Efficient dispatching rules for scheduling in a job shop. Int. J. Prod. Econ. 48(1), 87–105 (1997)
Jayamohan, M., Rajendran, C.: New dispatching rules for shop scheduling: a step forward. Int. J. Prod. Res. 38(3), 563–586 (2000)
Rajendran, C., Holthaus, O.: A comparative study of dispatching rules in dynamic flowshops and jobshops. Eur. J. Oper. Res. 116(1), 156–170 (1999)
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)
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
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)
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)
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)
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)
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)
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)
Durasević, M., Jakobović, D., Knežević, K.: Adaptive scheduling on unrelated machines with genetic programming. Appl. Soft Comput. 48, 419–430 (2016)
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)
Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Evol. Comput. 23(3), 343–367 (2015)
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
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
Luke, S., et al.: A Java-based evolutionary computation research system. https://cs.gmu.edu/~eclab/projects/ecj/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)