Skip to main content

Advertisement

SpringerLink
Log in

Optimization of job shop scheduling problems using teaching-learning-based optimization algorithm

  • Application Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

A job shop scheduling problem is one of the most difficult NP hard combinatorial optimization problems. In a job shop scheduling problem (JSSP), there are n jobs that should be processed on m machines. Each job consists of a predetermined sequence of task operations, each of which needs to be processed without interruption for a given period of time on a given machine. Tasks of the same job cannot be processed concurrently. In recent years, optimization algorithms such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS), ant colony optimization (ACO) particle swarm optimization (PSO) and artificial bee colony (ABC) have played a significant role in solving small-scale job shop scheduling problems. However, when the problem size grows, metaheuristic algorithms usually take excessive time to converge. In this study, a recently developed Teaching-Learning-Based Optimization (TLBO) method is proposed to solve the job shop scheduling problems to minimize the makespan. The proposed algorithm is tested on 58 job shop scheduling bench mark problems from OR Library and results are compared with the results obtained by using the other algorithms. It is concluded that the TLBO algorithm can be effectively used for job shop scheduling problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Zhou, H., Feng, Y., Han, L.: The hybrid heuristic genetic algorithm for job shop scheduling. Comput. Ind. Eng. 40, 191–200 (2011)

    Article  Google Scholar 

  2. Sha, D.Y., Cheng-Yu, H.: A hybrid particle swarm optimization for job shop scheduling problem. Comput. Ind. Eng. 51, 791–808 (2006)

    Article  Google Scholar 

  3. Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T., Chan, S.F.: Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: a case study based on the apparel industry. Comput. Ind. Eng. 50, 202–219 (2006)

    Article  Google Scholar 

  4. Seda, M.: Mathematical models of flow shop and job shop scheduling problems. World. Aca. Sci. Eng. Technol. 31, 122–127 (2007)

    Google Scholar 

  5. Moghaddas, R., Houshmand, M.: Job-shop scheduling problem with sequence dependent setup times. Multi. Conf. Eng. Comput. Sci. 2, 19–21 (2008)

    Google Scholar 

  6. Mahavi, M.M., Farzad, Z., Farzad, F.J.: A fuzzy modeling for single machine scheduling problem with deteriorating jobs. Int. J. Ind. Eng. Comput. 1, 147–156 (2010)

    Google Scholar 

  7. Kaschel, J., Teich, T., Kobernik, G., Meier, B.: Algorithms for the job shop scheduling problem—a comparison of different methods. European Symposium on Intelligent Techniques, June 3–4, Outdoor Academy of Crete, Greece. 10, (1998)

  8. Zhigang, L., Bin, J., Xingsheng, G.: A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan. App. Math. Comput. 183, 1008–1017 (2006)

    Article  Google Scholar 

  9. Lin, T.L., Shi, J.H., Tzong, W.K., Yuan, H.C.R.J., Jui, L.L., Kuo, I.H., Run, R.S.: An efficient job-shop scheduling algorithm based on particle swarm optimization. Exp. Syst. Appl. 37, 2629–2636 (2010)

    Article  Google Scholar 

  10. Huang, R.H.: Multi-objective job-shop scheduling with lot-splitting production. Int. J. Prod. Eco. 124, 206–213 (2010)

    Article  Google Scholar 

  11. Zhang, R., Cheng, W.: A hybrid immune simulated annealing algorithm for the job shop scheduling problem. Appl. Soft. Comput. 10, 79–89 (2010)

    Article  Google Scholar 

  12. Zhang, R., Cheng, W.: A hybrid local search algorithm for scheduling real-world job shops with batch-wise pending due dates. Eng. Appli. Arti. Intel. 25, 209–221 (2012)

    Article  Google Scholar 

  13. Anandaraman, C.: An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems. Int. J. Ind. Eng. Comput. 2, 749–764 (2011)

    Google Scholar 

  14. Zhang, R.: An artificial bee colony algorithm based on problem data properties for scheduling job shops. Proce. Eng. 23, 131–136 (2011)

    Article  Google Scholar 

  15. Veronique, S., Kjeld, C., Mario, V.: A hybrid single and dual population search procedure for the job shop scheduling problem. Eur. J. Oper. Res. 215, 512–523 (2011)

    Article  Google Scholar 

  16. Lei, W., Dun, B.T.: An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem. Exp. Syst. Appli. 38, 7243–7250 (2011)

    Article  Google Scholar 

  17. Gao, L., Zhang, G., Zhang, L., Xinyu, L.: An efficient Memetic algorithm for solving the job shop scheduling problem. Comput. Ind. Eng. 60, 699–705 (2011)

    Article  Google Scholar 

  18. Hsueh, C.C., Tsung, C.C., Li, C.F.: A two-stage hybrid memetic algorithm for multiobjective job shop scheduling. Exp. Syst. Appli. 38, 10983–10998 (2011)

    Article  Google Scholar 

  19. Lawrence, S.: Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh (1984)

    Google Scholar 

  20. Zhang, R., Shiji, S., Cheng, W.: A hybrid artificial bee colony algorithm for the job shop scheduling problem. Int. J. Prod. Econ (2012). doi:10.1016/j.ijpe.2012.03.035

    Google Scholar 

  21. Anan, B., Booncharoen, S., Tiranee, A.: Job shop scheduling with the best-so-far ABC. Eng. Appli. Arti. Intel. 25, 583–593 (2012)

    Article  Google Scholar 

  22. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Info. Sci. 183, 1–15 (2012)

    Article  Google Scholar 

  23. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aided Des. 43, 303–315 (2011)

    Article  Google Scholar 

  24. Rao, R.V., Patel, V.: An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int. J. Ind. Eng. Comput. 3, 535–560 (2012)

    Google Scholar 

  25. Rao, R.V., Kalyankar, V.D.: Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng. Appli. Arti. Intel. 26, 524–531 (2013)

    Article  Google Scholar 

  26. Rao, R.V., Patel, V.K.: An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica 20, 710–720 (2013)

    Google Scholar 

  27. Rao, R.V., Patel, V.K.: Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Int. J. Ind. Eng. Comput. 4, 29–50 (2013)

    Google Scholar 

  28. Zou, F., Wang, L., Hei, X., Chen, D., Wang, B.: Multi-objective optimization using teaching-learning-based optimization algorithm. Eng. Appl. Arti. Intel. 26, 1291–1300 (2013)

    Article  Google Scholar 

  29. Kellegoz, T., Toklu, B.: Comparing efficiencies of genetic crossover operations for one machine total weighted tardiness problem. Appl. Math. Comput. 199, 590–598 (2008)

    Article  Google Scholar 

  30. Hansen, P., Mladenovic, N., Perez, J.A.M.: Variable neighborhood search: methods and applications. 4 OR 6, 319–360 (2008)

  31. Applegate, D., Cook, W.: A computational study of the job-shop scheduling instance. ORSA J. Comput. 3, 149–156 (1991)

    Article  Google Scholar 

  32. Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job shop scheduling rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice-Hall, Englewood Cliffs (1963)

    Google Scholar 

  33. Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Mgt. Sci. 34, 391–401 (1988)

    Article  Google Scholar 

  34. Qing-dao-er-ji, R., Wang, Y.: A new hybrid genetic algorithm for job shop scheduling problem. Comput. Oper. Res. 39, 2291–2299 (2012)

    Article  Google Scholar 

  35. Asadzadeh, L., Zamanifar, K.: An agent-based parallel approach for the job shop scheduling problem with genetic algorithms. Math. Comput. Model. 52, 1957–1965 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, 395007, Gujarat, India

    H. S. Keesari & R. V. Rao

Authors
  1. H. S. Keesari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. V. Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. V. Rao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Keesari, H.S., Rao, R.V. Optimization of job shop scheduling problems using teaching-learning-based optimization algorithm. OPSEARCH 51, 545–561 (2014). https://doi.org/10.1007/s12597-013-0159-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-013-0159-9

Keywords

Search

Navigation