Skip to main content
Springer Nature Link
Log in

A New Hybrid GA/SA Algorithm for the Job Shop Scheduling Problem

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3448))

  • 945 Accesses

Abstract

Among the modern heuristic methods, simulated annealing (SA) and genetic algorithms (GA) represent powerful combinatorial optimization methods with complementary strengths and weaknesses. Borrowing from the respective advantages of the two paradigms, an effective combination of GA and SA, called Genetic Simulated Algorithm (GASA), is developed to solve the job shop scheduling problem (JSP). This new algorithm incorporates metropolis acceptance criterion into crossover operator, which could maintain the good characteristics of the previous generation and reduce the disruptive effects of genetic operators. Furthermore, we present two novel features for this algorithm to solve JSP. Firstly, a new full active schedule (FAS) based on the operation-based representation is presented to construct schedule, which can further reduce the search space. Secondly, we propose a new crossover operator, named Precedence Operation Crossover (POX), for the operation-based representation. The approach is tested on a set of standard instances and compared with other approaches. The Simulation results validate the effectiveness of the proposed algorithm.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of the International Conference on Genetic Algorithms and their Applications, pp. 136–149. Lawrence Erl-baum, Hillsdale (1985)

    Google Scholar 

  2. Bierwirth, C.: A generalized permutation approach to job shop scheduling with genetic al-gorithms. OR Spektrum 17, 87–92 (1995)

    Article  MATH  Google Scholar 

  3. Croce, F.D., Tadei, R., Volta, G.: A genetic algorithm for the job shop problem. Computers & Operations Research 22(1), 15–24 (1995)

    Article  MATH  Google Scholar 

  4. Laarhoven, P.V., Aarts, E., Lenstra, J.K.: Job shop scheduling by simulated annealing. Operations Research 40(1), 113–125 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  5. Aarts, E.H.L., van Laarhoven, P.J.M., Lenstra, J.K., Ulder, N.L.J.: A computational study of local search algorithms for Job Shop Scheduling. ORSA Journal on Computing 6, 118–125 (1994)

    MATH  Google Scholar 

  6. Taillard, E.D.: Parallel taboo search techniques for the job-shop scheduling problem. ORSA J. on Comput. 6(2), 108–117 (1994)

    MATH  Google Scholar 

  7. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Management Science 42(6), 797–813 (1996)

    Article  MATH  Google Scholar 

  8. Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: Past, present and future. Euro-pean Journal of Operational Research 113, 390–434 (1999)

    Article  MATH  Google Scholar 

  9. Blazewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: Conventional and new solution techniques. European Journal of Operational Research 93, 1–33 (1996)

    Article  MATH  Google Scholar 

  10. DeJong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Dissertation Abstracts International 36(10), 5140B(University Microfilms No.76-9381), Ph.D. Thesis, University of Michigan, Ann Arbor (1975)

    Google Scholar 

  11. Mahfoud, S.W., Goldberg, D.E.: Parallel Recombinative Simulated Annealing:A Genetic Algorithm. Parallel Computing 21, 1–28 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  12. Ingber, L., Rosen, B.: Genetic algorithms and very fast simulated reannealing: a comparison. Mathematical Computer Modeling 16(11), 87–100 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  13. Brown, D.E., Huntley, C.L., Spillane, A.R.: A Parallel Genetic Heuristic for the Quadratic Assignment Problem. In: Proceedings of the Third International Conference on Genetic Algorithms, Fairfax, VA, pp. 406–415 (1989)

    Google Scholar 

  14. Lin, F.T., Kao, C.Y., Hsu, C.C.: Incorporating Genetic Algorithms into Simulated Annealing. In: Proceeding of the Fourth International Symposium on Artificial Intelligence, pp. 290–297 (1991)

    Google Scholar 

  15. Goldberg, D.E.: A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems 4, 445–460 (1990)

    MATH  Google Scholar 

  16. Gen, M., Tsujimura, Y., Kubota, E.: Solving Job-Shop Scheduling Problems by Genetic Algorithm. In: Proceedings of the 1995 IEEE International Conference on Systems, Man, and Cybernetics. Institute of Electrical and Electronics Engineers, Vancouver, pp. 1577–1582 (1995)

    Google Scholar 

  17. Shi, G.Y., Iima, H., Sannomiya, N.: A new encoding scheme for Job Shop problems by Genetic Algorithm. In: Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, pp. 4395–4400 (1996)

    Google Scholar 

  18. Cheng, R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithms – I. Representation. Computers and Industrial Engineering 30(9), 83–97 (1996)

    Google Scholar 

  19. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)

    MATH  Google Scholar 

  20. Gonçalves, J.F.: A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem. AT&T Labs Research Technical Report TD-5EAL6J (September 2002)

    Google Scholar 

  21. Kobayashi, S., Ono, I., Yamamura, M.: An Efficient Genetic Algorithm for Job Shop Scheduling Problems. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 506–511 (1995)

    Google Scholar 

  22. Kolonko, M.: Some new results on simulated annealing applied to the job shop scheduling problem. European Journal of Operational Research 113, 123–136 (1999)

    Article  MATH  Google Scholar 

  23. Pezzella, F., Merelli, E.: A tabu search method guided by shifting bottleneck for the job shop scheduling problem. European Journal of Operational Research 120, 297–310 (2000)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, 430074, P.R. China

    Chaoyong Zhang, Peigen Li, Yunqing Rao & Shuxia Li

Authors
  1. Chaoyong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peigen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunqing Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuxia Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. SAP AG, Neurottstr. 16, P.O. Box, Walldorf, Germany

    Jens Gottlieb

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, C., Li, P., Rao, Y., Li, S. (2005). A New Hybrid GA/SA Algorithm for the Job Shop Scheduling Problem. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-31996-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25337-2

  • Online ISBN: 978-3-540-31996-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics