Genetic Programming with Pareto Local Search for Many-Objective Job Shop Scheduling

  • Atiya MasoodEmail author
  • Gang Chen
  • Yi Mei
  • Harith Al-Sahaf
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11919)


Genetic programming (GP) has been successfully used to automatically design effective dispatching rules for job shop scheduling (JSS) problems. It has been shown that hybridizing global search with local search can significantly improve the performance of many evolutionary algorithms such as GP because local search can directly improve the exploitation ability of these algorithms. Inspired by this, we aim to enhance the quality of evolved dispatching rules for many-objective JSS through hybridizing GP with Pareto Local Search (PLS) techniques. There are two challenges herein. First, the neighborhood structure in GP is not trivially defined. Second, the acceptance criteria during the local search for many-objective JSS has to be carefully designed to guide the search properly. In this paper, we propose a new algorithm that seamlessly integrates GP with Pareto Local Search (GP-PLS). To the best of our knowledge, it is the first time to combine GP with PLS for solving many-objective JSS. To evaluate the effectiveness of our new algorithm, GP-PLS is compared with the GP-NSGA-III algorithm, which is the current state-of-the-art algorithm for many-objective JSS. The experimental results confirm that the newly proposed method can outperform GP-NSGA-III thanks to the proper use of local search techniques. The sensitivity of the PLS-related parameters on the performance of GP-PLS is also experimentally investigated.


Many-objective optimization Genetic programming Pareto Local Search Evolutionary computation Job shop scheduling 


  1. 1.
    Błażewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: conventional and new solution techniques. Eur. J. Oper. Res. 93(1), 1–33 (1996)CrossRefGoogle Scholar
  2. 2.
    Chen, B., Zeng, W., Lin, Y., Zhang, D.: A new local search-based multiobjective optimization algorithm. IEEE Trans. Evol. Comput. 19(1), 50–73 (2015)CrossRefGoogle Scholar
  3. 3.
    Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)CrossRefGoogle Scholar
  4. 4.
    Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Anytime pareto local search. Eur. J. Oper. Res. 243(2), 369–385 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Holthaus, O., Rajendran, C.: Efficient jobshop dispatching rules: further developments. Prod. Plan. Control 11(2), 171–178 (2000)CrossRefGoogle Scholar
  6. 6.
    Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 28(3), 392–403 (1998)CrossRefGoogle Scholar
  7. 7.
    Masood, A., Chen, G., Mei, Y., Zhang, M.: Reference point adaption method for genetic programming hyper-heuristic in many-objective job shop scheduling. In: Liefooghe, A., López-Ibáñez, M. (eds.) EvoCOP 2018. LNCS, vol. 10782, pp. 116–131. Springer, Cham (2018). Scholar
  8. 8.
    Masood, A., Mei, Y., Chen, G., Zhang, M.: Many-objective genetic programming for job-shop scheduling. In: Proceedings of 2016 IEEE Congress on Evolutionary Computation. IEEE (2016)Google Scholar
  9. 9.
    Nguyen, S.: Automatic design of dispatching rules for job shop scheduling with genetic programming. Ph.D. thesis (2013)Google Scholar
  10. 10.
    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
  11. 11.
    Nguyen, S., Zhang, M., Johnston, M.: A genetic programming based hyper-heuristic approach for combinatorial optimisation. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1299–1306. ACM (2011)Google Scholar
  12. 12.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Dynamic multi-objective job shop scheduling: a genetic programming approach. In: Uyar, A., Ozcan, E., Urquhart, N. (eds.) Automated Scheduling and Planning. Studies in Computational Intelligence, vol. 505, pp. 251–282. Springer, Heidelberg (2013). Scholar
  13. 13.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic programming via iterated local search for dynamic job shop scheduling. IEEE Trans. Cybern. 45(1), 1–14 (2015)CrossRefGoogle Scholar
  14. 14.
    Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278–285 (1993)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Tsai, C.W., Rodrigues, J.J.P.C.: Metaheuristic scheduling for cloud: a survey. IEEE Syst. J. 8(1), 279–291 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, Technical report, pp. 1–30 (2008)Google Scholar
  18. 18.
    Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Atiya Masood
    • 1
    Email author
  • Gang Chen
    • 1
  • Yi Mei
    • 1
  • Harith Al-Sahaf
    • 1
  • Mengjie Zhang
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations