Skip to main content
SpringerLink
Log in

Brain Storm Optimization Algorithms for Flexible Job Shop Scheduling Problem

  • Chapter
  • First Online:
Brain Storm Optimization Algorithms

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 23))

Abstract

Scheduling is to determine when and who to process the task in order to minimize the makespan, the cost, and the tardiness/earliness of jobs, etc. Evolutionary algorithms (EAs) are very promising approaches for the scheduling problems due to its dynamic characteristics, multiple contradicting objectives and highly nonlinear constraints. Brain storm optimization (BSO) algorithm and its variations are new emerging evolutionary algorithms. In this chapter, our focus will be on BSOs for the flexible job shop scheduling problems (FJSP). First, FJSP will be formulated as a combinatorial optimization problem. Second, approaches for FJSP will be summarized briefly. Third, BSOs will be introduced and the key issues in the application of BSOs for FJSP will be emphasized. Fourth, four BSOs will be developed to solve FJSP and the details designed for FJSP will be illustrated. Their strength and weakness will be discussed as well. Finally, a group of experiments will be conducted to compare the four BSO algorithms. The results will be analyzed statistically. Finally, a conclusion will be summarized.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Brandimarte, P.: Routing and scheduling in a flexible job shop by tabu search. Ann. Oper. Res. 41(3), 157–183 (1993)

    Article  Google Scholar 

  2. Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45(4), 369–375 (1990)

    Article  MathSciNet  Google Scholar 

  3. Chen, H., Ihlow, J., Lehmann, C.: A genetic algorithm for flexible job-shop scheduling. Proc. IEEE Int. Conf. Robot. Autom. 2, 1120–1125 (1999)

    Article  Google Scholar 

  4. Chen, J.C., Wu, C., Chen, C., Chen, K.: Flexible job shop scheduling with parallel machines using genetic algorithm and grouping genetic algorithm. Expert Syst. Appl. 39, 10016–10021 (2012)

    Article  Google Scholar 

  5. Dauzère-Pérès, S., Paulli, J.: An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann. Oper. Res. 70, 281–306 (1997)

    Article  MathSciNet  Google Scholar 

  6. Gao, J., Sun, L., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput. Oper. Res. 35, 2892–2907 (2008)

    Article  MathSciNet  Google Scholar 

  7. Gong, G., Deng, Q., Gong, X., et al.: A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. J. Clean. Prod. 174, 560–576 (2018)

    Article  Google Scholar 

  8. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)

    Google Scholar 

  9. Hmida, A.B., Haouari, M., Huguet, M.J., et al.: Discrepancy search for the flexible job shop scheduling problem. Comput. Oper. Res. 37(12), 2192–2201 (2010)

    Google Scholar 

  10. Ho, N.B., Tay, J.C.: GENACE: an efficient cultural algorithm for solving the flexible job-shop problem. In: Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, vol. 2, pp. 1759–1766 (2004)

    Google Scholar 

  11. Kacem, I., Hammadi, S., Borne, P.: Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans. Syst. Man Cybern. C 32, 408–419 (2002)

    Article  Google Scholar 

  12. Li, J., Pan, Q.: Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl. Soft Comput. 12, 2896–2912 (2012)

    Article  Google Scholar 

  13. Li, X., Gao, L.: An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int. J. Prod. Econ. 174, 93–110 (2016)

    Article  Google Scholar 

  14. Loukil, T., Teghem, J., Tuyttens, D.: Solving multi-objective production scheduling problems using metaheuristics. Eur. J. Oper. Res. 161, 42–61 (2005)

    Article  MathSciNet  Google Scholar 

  15. Ma, Y., Chu, C., Zuo, C.: A survey of scheduling with deterministic machine availability constraints. Comput. Ind. Eng. 58(2), 199–211 (2010)

    Article  Google Scholar 

  16. Mati, Y., Rezg, N., Xie, X.: An integrated greedy heuristic for a flexible job shop scheduling problem. Proc. IEEE Int. Conf. Syst. Man Cybern. 4, 2534–2539 (2001)

    Google Scholar 

  17. Mokhtari, H., Hasani, A.: An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Comput. Chem. Eng. 104, 339–352 (2017)

    Article  Google Scholar 

  18. Paredis, J.: Exploiting constraints as background knowledge for genetic algorithms: a case-study for scheduling. In: Parallel Problem Solving from Nature 2, PPSN-II, Brussels, Belgium, September. DBLP, pp. 231–240 (1992)

    Google Scholar 

  19. Pezzella, F., Morganti, G., Ciaschetti, G.: A genetic algorithm for the flexible job-shop scheduling problem. Comput. Oper. Res. 35, 3202–3212 (2008)

    Article  Google Scholar 

  20. Piroozfard, H., Wong, K.Y., Wong, W.P.: Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resour. Conserv. Recycl. 128, 267–283 (2016)

    Article  Google Scholar 

  21. Shi, Y.: Brain Storm Optimization Algorithm. Lecture Notes in Computer Science, vol. 4, no. 3, pp. 303–309 (2011)

    Google Scholar 

  22. Singh, M.R., Mahapatra, S.S.: A quantum behaved particle swarm optimization for flexible job shop scheduling. Pergamon Press Inc. (2016)

    Google Scholar 

  23. Syswerda, G.: Scheduling optimization using genetic algorithms. In: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  24. Tay, J.C., Wibowo, D.: An effective chromosome representation for evolving flexible job shop schedules. In: Genetic and Evolutionary Computation—GECCO 2004. DBLP, 2004, pp. 210–221 (2004)

    Google Scholar 

  25. Wang, L., Wang, S., Xu, Y., Zhou, G., Liu, M.: A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Comput. Ind. Eng. 62, 917–926 (2012)

    Article  Google Scholar 

  26. Wu, X., Sun, Y.: A green scheduling algorithm for flexible job shop with energy-saving measures. J. Clean. Prod. 172, 3249–3264 (2018)

    Article  Google Scholar 

  27. Wu, X., Wu, S.: An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem. J. Intell. Manuf. 28(6), 1441–1457 (2017)

    Article  Google Scholar 

  28. Wu, X., Zhang, Z., Li, J.: A brain storm optimization algorithm integrating diversity and discussion mechanism for solving discrete production scheduling problem. Control Decis. 32(9), 1583–1590 (in Chinese) (2017)

    Google Scholar 

  29. Xia, W., Wu, Z.: An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput. Ind. Eng. 48, 409–425 (2005)

    Article  Google Scholar 

  30. Xing, L.N., Chen, Y.W., Wang, P., Zhao, Q.S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10, 888–896 (2010)

    Article  Google Scholar 

  31. Yang, Y., Shi, Y., Xia, S.: Advanced discussion mechanism-based brain storm optimization algorithm. Soft. Comput. 19(10), 2997–3007 (2015)

    Article  Google Scholar 

  32. Yazdani, M., Amiri, M., Zandieh, M.: Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Syst. Appl. 37(1), 678–687 (2010)

    Article  Google Scholar 

  33. Yuan, Y., Xu, H.: Flexible job shop scheduling using hybrid differential evolution algorithms. Comput. Ind. Eng. 65(2), 246–260 (2013)

    Article  Google Scholar 

  34. Zhang, C.-Y., Li, P., Rao, Y., Li, S.: A new hybrid GA/SA algorithm for the job shop scheduling problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. Lecture Notes in Computer Science, vol. 3448, pp. 246–259. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  35. Zhou, D., Shi, Y., Cheng, S.: Brain storm optimization algorithm with modified step-size and individual generation. In: Advances in Swarm Intelligence, 243–252. Springer, Heidelberg

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China Grant (Grant No. 50135024). The author also thanks the three anonymous reviewers for their constructive suggestions and comments to improve the quality of the work.

Author information

Authors and Affiliations

  1. Department of Logistic Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Xiuli Wu

Authors
  1. Xiuli Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuli Wu .

Editor information

Editors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  2. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wu, X. (2019). Brain Storm Optimization Algorithms for Flexible Job Shop Scheduling Problem. In: Cheng, S., Shi, Y. (eds) Brain Storm Optimization Algorithms. Adaptation, Learning, and Optimization, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-15070-9_10

Download citation

Publish with us

Policies and ethics

Search

Navigation