Skip to main content
SpringerLink
Log in
Book cover

International Conference on Advanced Intelligent Systems for Sustainable Development

AI2SD 2018: Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) pp 228–236Cite as

The Discrete Swallow Swarm Optimization for Flow-Shop Scheduling Problem

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 915))

Abstract

The flow-shop scheduling problem is a well-known problem in production system. The objective is minimizing the total time it takes to process the entire job called makespan. In order to solve this NP-hard problem, we approve a new adaptation approach based on the intelligent behaviors of swallows, it is the discrete swallow swarm optimization algorithm (DSSO) present a recent metaheuristic method used to solve a combinatorial problem. The proposed algorithm is tested on different benchmarks instances and compared with different proposed algorithms. The results demonstrate that the proposed algorithm is more efficient than the other compared algorithms. It can be used to solve large instances of flow shop scheduling problem effectively.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  1. Allahverdi, A., Gupta, J.N.D., Aldowaisan, T.: A review of scheduling research involving setup considerations. Omega 27, 219–239 (1999). https://doi.org/10.1016/S0305-0483(98)00042-5

    Article  Google Scholar 

  2. Allaoui, H., Artiba, A.: Scheduling two-stage hybrid flow shop with availability constraints. Comput. Oper. Res. 33, 1399–1419 (2006). https://doi.org/10.1016/j.cor.2004.09.034

    Article  MathSciNet  MATH  Google Scholar 

  3. Błażewicz, J., Ecker, K.H., Pesch, E., Schmidt, G., Weglarz, J.: Scheduling Computer and Manufacturing Processes. Springer-Verlag, Berlin Heidelberg (1996)

    Book  Google Scholar 

  4. Bouzidi, M., Riffi, M.E., Serhir, A.: Discrete particle swarm optimization for travelling salesman problems: new combinatorial operators. In: Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017), pp. 141–150. Springer, Cham (2017)

    Google Scholar 

  5. Bouzidi, S., Riffi, M.E.: Discrete swallow swarm optimization algorithm for travelling salesman problem. In: Proceedings of the 2017 International Conference on Smart Digital Environment, pp. 80–84. ACM, New York, NY, USA (2017)

    Google Scholar 

  6. Johnson, S.M.: Optimal two- and three-stage production schedules with setup times included. Nav. Res. Logist. Q. 1, 61–68 (1954). https://doi.org/10.1002/nav.3800010110

    Article  MATH  Google Scholar 

  7. Kaveh, A., Bakhshpoori, T., Afshari, E.: An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput. Struct. 143, 40–59 (2014). https://doi.org/10.1016/j.compstruc.2014.07.012

    Article  Google Scholar 

  8. Liao, C.-J., Tjandradjaja, E., Chung, T.-P.: An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Appl. Soft Comput. 12, 1755–1764 (2012). https://doi.org/10.1016/j.asoc.2012.01.011

    Article  Google Scholar 

  9. Lin, Q., Gao, L., Li, X., Zhang, C.: A hybrid backtracking search algorithm for permutation flow-shop scheduling problem. Comput. Ind. Eng. 85, 437–446 (2015). https://doi.org/10.1016/j.cie.2015.04.009

    Article  Google Scholar 

  10. Liu, Y.-F., Liu, S.-Y.: A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem. Appl. Soft Comput. 13, 1459–1463 (2013). https://doi.org/10.1016/j.asoc.2011.10.024

    Article  Google Scholar 

  11. Marichelvam, M.K., Prabaharan, T., Yang, X.S.: Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl. Soft Comput. 19, 93–101 (2014). https://doi.org/10.1016/j.asoc.2014.02.005

    Article  Google Scholar 

  12. Murata, T., Ishibuchi, H., Tanaka, H.: Genetic algorithms for flowshop scheduling problems. Comput. Ind. Eng. 30, 1061–1071 (1996). https://doi.org/10.1016/0360-8352(96)00053-8

    Article  Google Scholar 

  13. Neshat, M., Sepidnam, G., Sargolzaei, M.: Swallow swarm optimization algorithm: a new method to optimization. Neural Comput. Appl. 23, 429–454 (2013). https://doi.org/10.1007/s00521-012-0939-9

    Article  Google Scholar 

  14. Reeves, C.R.: A genetic algorithm for flowshop sequencing. Comput. Oper. Res. 22, 5–13 (1995). https://doi.org/10.1016/0305-0548(93)E0014-K

    Article  MATH  Google Scholar 

  15. Sayoti, F.: Golden Ball Algorithm for solving Flow Shop Scheduling Problem. Int. J. Interact. Multimedia Artif. Intell. 4, 15–18 (2016)

    Google Scholar 

  16. Sotskov, Y.N., Shakhlevich, N.V.: NP-hardness of shop-scheduling problems with three jobs. Discrete Appl Math 59, 237–266 (1995). https://doi.org/10.1016/0166-218X(95)80004-N

    Article  MathSciNet  MATH  Google Scholar 

  17. Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64, 278–285 (1993). https://doi.org/10.1016/0377-2217(93)90182-M

    Article  MATH  Google Scholar 

  18. Yagmahan, B., Yenisey, M.M.: Ant colony optimization for multi-objective flow shop scheduling problem. Comput. Ind. Eng. 54, 411–420 (2008). https://doi.org/10.1016/j.cie.2007.08.003

    Article  Google Scholar 

  19. Zhang, C., Sun, J., Zhu, X., Yang, Q.: An improved particle swarm optimization algorithm for flowshop scheduling problem. Inf. Process. Lett. 108, 204–209 (2008). https://doi.org/10.1016/j.ipl.2008.05.010

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LAROSERI Laboratory, Department of Computer Science, Chouaib Doukkali University, El Jadida, Morocco

    Safaa Bouzidi, Mohammed Essaid Riffi & Morad Bouzidi

  2. Department of Mathematics, Chouaib Doukkali University, El Jadida, Morocco

    Mohammed Moucouf

Authors
  1. Safaa Bouzidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Essaid Riffi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Morad Bouzidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammed Moucouf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Safaa Bouzidi .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bouzidi, S., Riffi, M.E., Bouzidi, M., Moucouf, M. (2019). The Discrete Swallow Swarm Optimization for Flow-Shop Scheduling Problem. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_20

Download citation

Publish with us

Policies and ethics

Search

Navigation