Skip to main content
SpringerLink
Log in

Finding multiple solutions in job shop scheduling by niching genetic algorithms

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

Abstract

The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view.

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

Similar content being viewed by others

References

  • Adams, J., Balas, E. and Zawack, D. (1988) The shifting bottleneck procedure for job shop scheduling. Management Science, 34, 391-401.

    Google Scholar 

  • Balas, E. (1969) Machine sequencing via disjunctive graphs: An implicit enumeration algorithm. Operations Research, 17, 941-957.

    Google Scholar 

  • Baker, J. R. and McMahon, G. B. (1985) Scheduling the general job shop. Management Science, 31, 594-598.

    Google Scholar 

  • Beasley, D., Bull, D. R. and Martin, R. R. (1993) A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1, 101-125.

    Google Scholar 

  • Brucker, P. (1997) Scheduling Algorithms, 2nd Edn, Springer-Verlag, Berlin, Germany.

    Google Scholar 

  • Bruns, R. (1993) Direct chromosome representation and advanced genetic operators for production scheduling. Proceedings of the Fifth International Conference on Genetic Algorithms, Stephanie Forrest (ed.), Kaufmann, San Mateo, California, USA, pp. 352-359.

    Google Scholar 

  • Carlier, J. and Pinson, E. (1989) An algorithm for solving the job shop problem. Management Science, 35, 164-176.

    Google Scholar 

  • Cedeño, W., Vemuri, V. R. and Slezak, T. (1995) Multiniche crowding in genetic algorithms and its application to the assembly of DNA restriction-fragments. Evolutionary Computation, 2, 321-345.

    Google Scholar 

  • Conway, R. W., Maxwell, W. L. and Miller, L. W. (1967) Theory of Scheduling, Addison-Wesley, Massachusetts, USA.

    Google Scholar 

  • Dell'Amico, M. and Trubian, M. (1993) Applying Tabu search to the job shop scheduling problem. Annals of Operations Research, 41, 231-252.

    Google Scholar 

  • Della, F., Tadeis, R. and Volta, G. (1995) A genetic algorithm for the job shop problem. Computers and Operations Research, 22, 15-24.

    Google Scholar 

  • Fang, H., Ross, P. and Corne, D. (1993) A promising genetic algorithm aproach to job shop scheduling, rescheduling, and open shop scheduling problems, in Proceedings of the Fifth International Conference on Genetic Algorithms, Stephanie Forrest (ed.), Kaufmann, San Mateo, California, USA, pp. 375-382.

    Google Scholar 

  • Fogel, D. B. (ed.) (1998) Evolutionary Computation, The Fossil Record (Selected readings on the history of evolutionary computation), IEEE Press, NY, USA.

    Google Scholar 

  • French, S. (1982) Sequencing and Scheduling: An Introduction to the Mathematics of the Job Shop, Ellis Horwood, Chichester, USA.

    Google Scholar 

  • Garey, M. and Johnson, D. (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, NY, USA.

    Google Scholar 

  • Glover, F. and Laguna, M. (1997) Tabu Search, Kluwer Academic, Boston, USA.

    Google Scholar 

  • Goldberg, D. E. and Richardson, J. (1987) Genetic algorithms with sharing for multimodal function optimization, in Proceedings of the Second International Conference on Genetic Algorithms, pp. 41-49.

  • Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Massachusetts, USA.

    Google Scholar 

  • Greenberg, H. (1968) A branch and bound solution to the general scheduling problem. Operations Research, 16, 353-361.

    Google Scholar 

  • Harik, G. (1995) Finding multiple solutions using restricted tournament selection, in Proceedings of the Sixth International Conference on Genetic Algorithms, Eschelman, L. (ed.), Kaufmann, USA, pp. 24-31.

    Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D. Jr. and Vecchi, M. P. (1983) Optimization by simulated annealing. Science, 220, 671-680.

    Google Scholar 

  • Kobayashi, S., Ono, I. and Yamamura, M. (1995) An efficient genetic algorithm for job shop scheduling problems, in Proceedings of the Sixth International Conference on Genetic Algorithms, Eschelman, L. (ed.), Kaufmann, San Francisco, California, USA, pp. 506-511.

    Google Scholar 

  • Mahfoud, S. W. (1992) Crowding and preselection revisited. Parallel Problem Solving from Nature II, Männer, R. and Manderick, B. (eds), Elsevier, pp. 27-36.

  • Mattfeld, D. C. (1995) Evolutionary Search and the Job Shop, Investigations on Genetic Algorithms for Production Scheduling, Springer, Berlin, Germany.

    Google Scholar 

  • McMahon, G. and Florian, M. (1975) On scheduling with ready times and due date to minimize maximum lateness. Operations Research, 23, 475-482.

    Google Scholar 

  • Michalewicz, Z. (1995) Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, Germany.

    Google Scholar 

  • Nakano, R. and Yamada, T. (1991) Conventional genetic algorithms for job shop problems, in Proceedings of the Fourth International Conference on Genetic Algorithms, Belew, R. and Booker, L. B. (co-ed.), Kaufmann, San Mateo, California, USA, pp. 474-479.

    Google Scholar 

  • Nowicki, E. and Smutnicki, C. (1996) A fast tabu search algorithm for the job shop problem. Management Science, 42, 797-813.

    Google Scholar 

  • Oei, C. K., Goldberg, D. E. and Chang, S. J. (1991) Tournament selection, niching and the preservation of diversity. IlliGAL Report No. 91011, University of Illinois, USA.

    Google Scholar 

  • Panwalkar, S. S. and Iskander, W. (1977) A survey of scheduling rules. Operations Research, 25, 45-61.

    Google Scholar 

  • P\'etrowski, A. (1996) Clearing procedure as a niching method for genetic algorithms, in Proc. 1996 IEEE Int. Conf. Evolutionary Computation, Nagoya, Japan, pp. 798-803.

  • Ramalhinho, H., Marti, O. and Stützle, T. (2000) Iterated local search. Economic Working Papers Series, Universitat Pompeu Fabra (To appear in the Metaheuritics book, Glover, F. and Kochenberger, G. eds.).

  • Sareni, B. and Krahenbuhl, L. (1998) Fitness sharing and niching methods revised. IEEE Transations on Evolutionary Computation, 2, 97-106.

    Google Scholar 

  • Stützle, T. (1998) Local Search Algorithms for Combinatorial Problems\3-Analysis, Improvements, and New Applications, PhD. thesis, Computer Science Departmentm Darmstadt University of Technology, Darmstadt, Germany.

    Google Scholar 

  • Van Laarhoven, P. J. M., Aarts, E. H. L. and Lenstra, J. K. (1992) Job shop scheduling by simulated annealing. Operations Research, 40, 112-129.

    Google Scholar 

  • Wang, L. and Zheng, D.-Z. (2001) An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research, 28, 585-596.

    Google Scholar 

  • Yamada, T. and Nakano, R. (1992) A genetic algorithm applicable to large-scale job shop problems. Parallel Problem Solving from Nature II, Männer, R. and Manderick, B. (eds.), Elsevier Science, Amsterdam, The Netherlands, pp. 281-290.

    Google Scholar 

  • Yang, S. and Wang, D. (2000) Constraint satisfaction adaptive neural network and heuristics combined approach for generalized job-shop scheduling. IEEE Trans. on Neural Networks 11, pp. 474-486.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Group, School of Industrial Engineering, University of Valladolid, 47011–, Valladolid, Spain

    E. Pérez & C. Hernández

  2. Department of Computer Science and Artificial Intelligence, University of Granada, 18071–, Granada, Spain

    F. Herrera

Authors
  1. E. Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pérez, E., Herrera, F. & Hernández, C. Finding multiple solutions in job shop scheduling by niching genetic algorithms. Journal of Intelligent Manufacturing 14, 323–339 (2003). https://doi.org/10.1023/A:1024649709582

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1024649709582

Search

Navigation