Skip to main content
Log in

A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

We propose a new hierarchical heuristic algorithm for multi-objective flexible job-shop scheduling problems. The proposed method is an adaptation of the Newton’s method for continuous multi-objective unconstrained optimization problems, belonging to the class of multi-criteria descent methods. Numerical experiments with the proposed method are presented. The potential of the proposed method is demonstrated by comparing the obtained results with the known results of existing methods that solve the same test instances.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Baykasoğlu, A., Özbakir, L., & Sönmez, A. I. (2004). Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling. Journal of Intelligent Manufacturing, 15, 777–785.

    Article  Google Scholar 

  • Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by taboo search. Annals of Operations Research, 41, 157–183.

    Article  Google Scholar 

  • Bruker, P., & Schlie, R. (1990). Job-shop scheduling with multi-purpose machine. Computing, 45, 369–375.

    Article  Google Scholar 

  • Çaliş, B., & Bulkan, S. (2013). A research survey: Review of AI solution strategies of job shop scheduling problem. Journal of Intelligent Manufacturing, doi:10.1007/s10845-013-0837-8

  • Dauzère-Pérès, S., & Paulli, J. (1997). An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Annals of Operations Research, 70, 281–306.

    Article  Google Scholar 

  • Fattahi, P., Mehrabad, M. S., & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 18, 331–342.

    Article  Google Scholar 

  • Fliege, J., Drummond, L. M. G., & Svaiter, B. (2009). Newton’s method for multiobjective optimization. SIAM Journal on Optimization, 20(2), 602–626.

    Article  Google Scholar 

  • Gao, J., Sun, L., & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers and Operations Research, 35, 2892–2907.

    Article  Google Scholar 

  • Hurink, E., Jurisch, B., & Thole, M. (1994). Tabu search for the job shop scheduling problem with multi-purpose machines. Operations Research Spektrum, 15, 205–215.

    Article  Google Scholar 

  • Hsu, T., Dupas, R., Jolly, D. & Goncalves, G. (2002). Evaluation of mutation heuristics for solving a multiobjective flexible job shop by an evolutionary algorithm. Systems, Man and Cybernetics IEEE International Conference on, vol. 5, p. 6, 6–9 Oct.

  • Kacem, I., Hammadi, S., & Borne, P. (2002a). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, 32, 1–13.

    Article  Google Scholar 

  • Kacem, I., Hammadi, S., & Borne, P. (2002b). Pareto-optimality approach for flexible job-shop scheduling problems: Hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60, 245–276.

    Article  Google Scholar 

  • Konak, A., Coit, D., & Smith, A. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91, 992–1007.

    Article  Google Scholar 

  • Liu, D., Yan, P., & Yu, J. (2009). Development of a multiobjective GA for advanced planning and scheduling problem. The International Journal of Advanced Manufacturing Technology, 42, 974–992.

    Google Scholar 

  • Mastrolilli, M., & Gambardella, L. M. (2000). Effective neighborhood functions for the flexible job shop problem. Journal of Scheduling, 3, 3–20.

    Article  Google Scholar 

  • Moslehi, M., & Mahnam, M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics, 129, 14–22.

    Article  Google Scholar 

  • Nie, L., Gao, L., Li, P., & Li, X. (2013). A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. Journal of Intelligent Manufacturing, 24, 763–774.

    Google Scholar 

  • Scrich, C. R., Armentano, V. A., & Laguna, M. (2004). Tardiness minimization in flexible job shop: A tabu search approach. Journal of Intelligent Manufacturing, 15, 103–115.

    Article  Google Scholar 

  • Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problem. Computers and Industrial Engineering, 48, 409–425.

    Google Scholar 

  • Zhang, H., & Gen, M. (2005). Multistage-based genetic algorithm for flexible job-shop scheduling problem. Journal of Complexity International, 11, 223–232.

    Google Scholar 

  • Zhang, H., Gen, M., & Seo, Y. (2006). An effective coding approach for multiobjective integrated resource selection and operation sequences problem. Journal of Intelligent Manufacturing, 17, 385–397.

    Article  Google Scholar 

  • Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers and Industrial Engineering, 56, 1309–1318.

    Article  Google Scholar 

  • Zribi, N., El Kamel, A., & Borne, P. (2006). Total tardiness in a flexible job-shop. Multiconference on Computational Engineering in Systems Applications, 2, 1543–1549.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fernanda M. P. Raupp.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pérez, M.A.F., Raupp, F.M.P. A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem. J Intell Manuf 27, 409–416 (2016). https://doi.org/10.1007/s10845-014-0872-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0872-0

Keywords

Navigation