Skip to main content
SpringerLink
Log in

An Evolutionary Algorithm Based Hyper-heuristic for the Job-Shop Scheduling Problem with No-Wait Constraint

  • Conference paper
  • First Online:
Harmony Search and Nature Inspired Optimization Algorithms

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 741))

Abstract

In this paper, we developed an evolutionary algorithm with guided mutation (EA/G) based hyper-heuristic for solving the job-shop scheduling problem with no-wait constraint (JSPNW). The JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. This problem is a typical \(\mathcal {NP}\)-hard problem. The hyper-heuristic algorithm comprises of two level frameworks. In the high-level, an evolutionary algorithm is employed to explore the search space. The low-level, which is comprised of generic as well as problem-specific heuristics such as guided mutation, multi-insert points and multi-swap. EA/G is a recent addition to the class of evolutionary algorithm that can be considered as a hybridization of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs), and which tries to overcome the shortcomings of both. In GAs, the location information of the solutions found so far is directly used to generate offspring. On the other hand, EDAs use global statistical information to generate new offspring. In EDAs the global statistical information is stored in the form probability vector, and a new offspring is generated by sampling this probability vector. We have compared our approach with the state-of-the-art approaches. The computational results show the effectiveness of our approach.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lenstra, J.K., Rinnooy Kan, A.H.G., Brucker, P.: Complexity of machine scheduling problems. Ann. Discret. Math. 1, 343–362 (1977)

    Article  MathSciNet  Google Scholar 

  2. Dorn, J., Shams, R.: Scheduling high-grade steelmaking. IEEE Expert 11, 28–35 (1996)

    Article  Google Scholar 

  3. Hall, N.J., Sriskandarajah, C.: A survey on machine scheduling problems with blocking and no-wait in process. Oper. Res. 44, 510–525 (1996)

    Article  MathSciNet  Google Scholar 

  4. Mascis, A., Pacciarelli, D.: Job-shop scheduling with blocking and no-wait constraints. Eur. J. Oper. Res. 143, 498–517 (2002)

    Article  MathSciNet  Google Scholar 

  5. Roy, B., Sussmann, B.: Les problèmes d’ordonnancement avec constraintes disjonctives. SEMA, Note D.S., No. 9, Paris (1964)

    Google Scholar 

  6. Macchiaroli, R., Molè, S., Riemma, S., Trifiletti, L.: Design and implementation of a Tabu search algorithm to solve the no-wait job-shop scheduling problem. In: Proceeding of the CESA96, pp. 467–472 (1996)

    Google Scholar 

  7. Brizuela, C.A., Zhao, Y., Sannomiya, N.: No-wait and blocking job-shops: challenging problems for GA’s. In: IEEE International Conference on Systems, man, and cybernetics, pp. 2349–2354. IEEE, New York (2001)

    Google Scholar 

  8. Schuster, C.J., Framinan, J.M.: Approximate procedures for no-wait job shop scheduling. Oper. Res. Lett. 31, 308–318 (2003)

    Article  MathSciNet  Google Scholar 

  9. Schuster, C.J.: No-wait job shop scheduling: Tabu search and complexity of subproblems. Math. Methods Oper. Res. 63, 473–491 (2006)

    Article  MathSciNet  Google Scholar 

  10. Zhu, J., Li, X., Wang, Q.: Complete local search with limited memory algorithm for no-wait job shops to minimize makespan. Eur. J. Oper. Res. 198, 378–386 (2009)

    Article  MathSciNet  Google Scholar 

  11. Zhu, J., Li, X.: An effective meta-heuristic for no-wait job shops to minimize makespan. IEEE Trans. Autom. Sci. Eng. 9, 189–198 (2012)

    Google Scholar 

  12. Sahni, S., Cho, Y.: Complexity of scheduling shops with no wait in process. Math. Oper. Res. 4, 448–457 (1979)

    Article  MathSciNet  Google Scholar 

  13. Schuster, C.: No-wait job shop scheduling: complexity under local search. Ph.D. thesis, Dissertation Thesis, University of Duisburg-Essen, Germany (2003)

    Google Scholar 

  14. Sriskandarajah, C., Ladet, P.: Some no-wait shops scheduling problems: complexity aspect. Eur. J. Oper. Res. 24(3), 424–438 (1986)

    Article  MathSciNet  Google Scholar 

  15. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  16. Denzinger, J., Fuchs, M., Fuchs, M.: High performance ATP systems by combining several AI methods. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence - Vol. 1, IJCAI’97, pp. 102–107. San Francisco, CA, USA. Morgan Kaufmann Publishers Inc., Burlington (1997)

    Google Scholar 

  17. Cowling, P., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit, pp. 176–190. Springer, Berlin (2001)

    MATH  Google Scholar 

  18. Wang, S., Wang, L., Zhou, G., Xu, Y.: An Estimation of Distribution Algorithm for the Flexible Job-Shop Scheduling Problem, pp. 9–16. Springer, Berlin (2012)

    Google Scholar 

  19. Salhi, A., Rodríguez, J.A.V., Zhang, Q.: An estimation of distribution algorithm with guided mutation for a complex flow shop scheduling problem. In: Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 570–576. ACM, New York (2007)

    Google Scholar 

  20. He, X.-J., Zeng, J.-C., Xue, S.-D., Wang, L.-F.: An Efficient Estimation of Distribution Algorithm for Job Shop Scheduling Problem, pp. 656–663. Springer, Berlin (2010)

    Google Scholar 

  21. Zhang, Q., Sun, J., Tsang, E.: An evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans. Evol. Comput. 9, 192–200 (2005)

    Article  Google Scholar 

  22. Chaurasia, S.N., Singh, A.: A hybrid heuristic for dominating tree problem. Soft. Comput. 20(1), 377–397 (2016)

    Article  Google Scholar 

  23. Chaurasia, S.N., Singh, A.: A hybrid evolutionary algorithm with guided mutation for minimum weight dominating set. Applied Intelligence 43(3), 512–529 (2015)

    Article  Google Scholar 

  24. Chaurasia, S.N., Sundar, S., Singh, A.: A hybrid evolutionary approach for set packing problem. Opsearch 52(2), 271–284 (2015)

    Article  MathSciNet  Google Scholar 

  25. Chaurasia, S.N., Singh, A.: Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem. Appl. Soft Comput. J. 52, 725–747 (2017)

    Article  Google Scholar 

  26. Sundar, S., Suganthan, P.N., Jin, C.T., Xiang, C.T., Soon, C.C.: A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint. Soft. Comput. 21(5), 1193–1202 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the grant [13AWMP-B066744-01] from the Advanced Water Management Research Program funded by the Ministry of Land, Infrastructure, and Transportation of the Korean government.

Author information

Authors and Affiliations

  1. Research Center for Disaster Prevention Science and Technology, Korea University, Seoul, 136-713, South Korea

    Sachchida Nand Chaurasia, Donghwi Jung & Ho Min Lee

  2. Department of Computer Applications, National Institute of Technology, Raipur, India

    Shyam Sundar

  3. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 136-713, South Korea

    Joong Hoon Kim

Authors
  1. Sachchida Nand Chaurasia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shyam Sundar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donghwi Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ho Min Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joong Hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joong Hoon Kim .

Editor information

Editors and Affiliations

  1. School of Engineering and Technology, BML Munjal University, Gurgaon, Haryana, India

    Neha Yadav

  2. Department of Sciences and Humanities, National Institute of Technology, Srinagar, Uttarakhand, India

    Anupam Yadav

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaurasia, S.N., Sundar, S., Jung, D., Lee, H.M., Kim, J.H. (2019). An Evolutionary Algorithm Based Hyper-heuristic for the Job-Shop Scheduling Problem with No-Wait Constraint. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_25

Download citation

Publish with us

Policies and ethics

Search

Navigation