Advertisement

Creating a Multi-iterative-Priority-Rule for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming

  • Georg E. A. FroehlichEmail author
  • Guenter Kiechle
  • Karl F. Doerner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)

Abstract

Genetic programming is used to create Priority Rules (PR) for the Job Shop Scheduling Problem with the aim of minimizing the weighted sum of tardy jobs. Four types of structures are used for the PR: Normal PR, Iterative-PR (IPR), Multi-PR (MPR), and Multi-Iterative-PR (MIPR). These are then compared among one another and with classical PR like shortest-processing-time. A modern metaheuristic based on local search using disjunct graphs and critical paths is used to solve the static problem as a benchmark. The results show that all types provide better results than classical PR and that with and without time limit the types from best to worst are: MIPR, MPR, IPR, and PR. The gaps to the metaheuristic are also reported.

References

  1. 1.
    Dimopoulos, C., Zalzala, A.M.S.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv. Eng. Softw. 32, 489–498 (2001)CrossRefGoogle Scholar
  2. 2.
    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).  https://doi.org/10.1007/11729976_7CrossRefGoogle Scholar
  3. 3.
    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).  https://doi.org/10.1007/978-3-540-71605-1_30CrossRefGoogle Scholar
  4. 4.
    Miyashita, K.: Job-shop scheduling with genetic programming. In: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, pp. 505–512 (2000)Google Scholar
  5. 5.
    Nguyen, S., Zhang, M., Johnston, M., Chen Tan, K.: Learning iterative dispatching rules for job shop scheduling with genetic programming. Int. J. Adv. Manuf. Technol. 67, 85–100 (2013)CrossRefGoogle Scholar
  6. 6.
    Branke, J., Nguyen, S., Pickardt, C.W., Mengjie, Z.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2016)CrossRefGoogle Scholar
  7. 7.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: 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)CrossRefGoogle Scholar
  8. 8.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevoluation genetic programming. IEEE Trans. Evol. Compu. 18(2), 193–208 (2014)CrossRefGoogle Scholar
  9. 9.
    Giffler, B., Thompson, G.L.: Algorithms for solving production-scheduling problems. Oper. Res. 8, 487–503 (1959)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wagner S. et al.: Architecture and Design of the HeuristicLab Optimization Environment. In: Klempous R., Nikodem J., Jacak W., Chaczko Z. (eds) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol 6. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Taillard, E.: Benchmarks for basic scheduling problems. Euro. J. Oper. Res. 64(2), 278–285 (1993)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Mati, Y., Dauzere-Peres, S., Lahlou, C.: A general approach for optimizing regular criteria in the job-shop scheduling problem. Euro. J. Oper. Res. 212, 33–42 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Singer, M., Pinedo, M.: Computational study of branch and bound techniques for minimizing the total weighted tardiness in job shops. IIE Trans. 29, 109–119 (1998)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Georg E. A. Froehlich
    • 1
    Email author
  • Guenter Kiechle
    • 1
  • Karl F. Doerner
    • 1
    • 2
  1. 1.Faculty of Business AdministrationUniversity of ViennaViennaAustria
  2. 2.Data ScienceUniversity of ViennaViennaAustria

Personalised recommendations