Skip to main content
SpringerLink
Log in

An improved artificial bee colony algorithm for the blocking flowshop scheduling problem

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion.

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

  1. Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM (2010) A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Comput Oper Res 37(3):509–520

    Article  MathSciNet  MATH  Google Scholar 

  2. Storn R, Price K (1997) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  3. Allahverdi A, Ng CT, Cheng TCE et al (2008) A survey of scheduling problems with setup times or costs. Eur J Oper Res 187:985–1032

    Article  MathSciNet  MATH  Google Scholar 

  4. Wang L, Pan QK, Tasgetiren MF (2010) Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithms. Expert Syst Appl 37:7929–7936

    Article  Google Scholar 

  5. McCormich ST, Pinedo ML, Shenker S, Wolf B (1989) Sequencing in an assembly line with blocking to minimize cycle time. Oper Res 37:925–936

    Article  Google Scholar 

  6. Leisten R (1990) Flowshop sequencing problems with limited buffer storage. Int J Prod Res 28:2085–2100

    Article  MATH  Google Scholar 

  7. Nawaz M, Enscore EEJ, Ham I (1983) A heuristic algorithm for them-machine, n-job flow shop sequencing problem. Int J Manag Sci 11:91–95

    Google Scholar 

  8. Ribas I, Companys R, Tort-Martorell X (2011) An iterated greedy algorithm for the flowshop scheduling problem with blocking. Omega 39:293–301

    Article  Google Scholar 

  9. Kim Y (1993) Heuristics for flowshop scheduling problems minimizing mean tardiness. J Oper Res Soc 44:19–28

    MATH  Google Scholar 

  10. Ronconi DP, Armentano VA (2001) Lower bounding schemes for flowshops with blocking in-process. J Oper Res Soc 52:1289–1297

    Article  MATH  Google Scholar 

  11. Ronconi DP (2004) A note on constructive heuristics for the flowshop problem with blocking. Int J Prod Econ 87:39–48

    Article  Google Scholar 

  12. Ronconi DP (2005) A branch-and-bound algorithm to minimize the makespan in a flowshop problem with blocking. Ann Oper Res 138:53–65

    Article  MathSciNet  MATH  Google Scholar 

  13. Li XP, Wang Q, Wu C (2009) Efficient composite heuristics for total flowtime minimization in permutation flow shops. Omega 37:155–164

    Article  MathSciNet  Google Scholar 

  14. Croce FD, Ghirardi M, Tadei R (2002) An improved branch-and-bound algorithm for the two machine total completion time flow shop problem. Eur J Oper Res 139:293–301

    Article  MATH  Google Scholar 

  15. Caraffa V, Ianes S, Bagchi TP, Sriskandarajah C (2001) Minimizing makespan in a blocking flowshop using genetic algorithms. Int J Prod Econ 70:102–115

    Article  Google Scholar 

  16. Wang L, Zhang I, Zheng D (2006) An effective hybrid genetic algorithm for flow shop scheduling with limited buffers. Comput Oper Res 33:2960–2971

    Article  MathSciNet  MATH  Google Scholar 

  17. Grabowski J, Pempera J (2007) The permutation flow shop problem with blocking, a tabu search approach. Omega 35:302–311

    Article  Google Scholar 

  18. Pan QK, Tasgetiren MF, Liang YC (2008) A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Comput Ind Eng 55:795–816

    Article  Google Scholar 

  19. Pan QK, Wang L (2008) No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm. Int J Adv Manuf Technol 39:796–807

    Article  Google Scholar 

  20. Pan QK, Wang L, Tasgetirend MF, Zhao BH (2008) A hybrid discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem with makespan criterion. Int J Adv Manuf Technol 38:337–347

    Article  Google Scholar 

  21. Liang JJ, Pan QK, Chen TJ, Wang L (2011) Solving the blocking flow shop scheduling problem by a dynamic multi-swarm particle swarm optimizer. Int J Adv Manuf Technol 55:755–762

    Article  Google Scholar 

  22. Pan QK, Wang L, Gao L, Li JQ (2011) An effective shuffled frog-leaping algorithm for lot-streaming flow shop scheduling problem. Int J Adv Manuf Technol 52:699–713

    Article  Google Scholar 

  23. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical Report TR06. Computer Engineering Department, Erciyes University, Turkey

    Google Scholar 

  24. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  25. Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9:625–631

    Article  Google Scholar 

  26. Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87:861–870

    Article  Google Scholar 

  27. Pan QK, Tasgetiren MF, Suganthan PN, Chua TJ (2011) A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf Sci 181:2455–2468

    Article  MathSciNet  Google Scholar 

  28. Li JQ, Pan QK, Gao KZ (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol. doi:10.1007/s00170-010-3140-2

  29. Han YY, Duan JH, Zhang M (2011) Apply the discrete artificial bee colony algorithm to the blocking flow shop with makespan criterion. In Proceeding of 2011 Chinese Control and Decision Conference, 2135–2139

  30. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    Article  MathSciNet  MATH  Google Scholar 

  31. Wang L (2003) Shop scheduling with genetic algorithms. Tsinghua University Press, Beijing

    Google Scholar 

  32. Ruben R, Stutzle T (2008) An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur J Oper Res 187:1143–1159

    Article  MATH  Google Scholar 

  33. Pan QK, Ruben R (2012) An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. OMEGA-The Int J Manag Sci 40:166–180

    Article  Google Scholar 

  34. Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64:278–285

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This research is partially supported by the National Science Foundation of China under grants 60874075, 61174187, and 61104179; Science Foundation of Shandong Province, China (BS2010DX005); Postdoctoral Science Foundation of China under grant 20100480897; and Science Research and Development of the Provincial Department of Public Education of Shandong under grant J09LG29 and J10LG67.

Author information

Authors and Affiliations

  1. College of Computer Science, Liaocheng University, Liaocheng, 252059, People’s Republic of China

    Yu-Yan Han, Quan-Ke Pan & Jun-Qing Li

  2. School of Mathematics Science, Liaocheng University, Liaocheng, 252059, People’s Republic of China

    Hong-yan Sang

Authors
  1. Yu-Yan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quan-Ke Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun-Qing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong-yan Sang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quan-Ke Pan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Han, YY., Pan, QK., Li, JQ. et al. An improved artificial bee colony algorithm for the blocking flowshop scheduling problem. Int J Adv Manuf Technol 60, 1149–1159 (2012). https://doi.org/10.1007/s00170-011-3680-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3680-0

Keywords

Search

Navigation