Skip to main content

Advertisement

Log in

A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem

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

Abstract

Biogeography-based optimization (BBO) algorithm is a new kind of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

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

    Article  MathSciNet  MATH  Google Scholar 

  2. Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Annual Operation Research 41:157–183

    Article  MATH  Google Scholar 

  3. Barnes JW, Chambers JB (1996) Flexible job shop scheduling by tabu search. Graduate Program in Operations Research and Industrial Engineering. University of Texas, Austin, Technical Report Series, ORP96-09

  4. Xia WJ, Wu ZM (2005) An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computer and Industrial Engineering 48(2):409–425

    Article  MathSciNet  Google Scholar 

  5. Hurink E, Jurisch B, Thole M (1994) Tabu search for the job shop scheduling problem with multi-purpose machine. Operations Research Spektrum 15(4):205–215

    Article  MathSciNet  MATH  Google Scholar 

  6. Scrich CR, Armentano VA, Laguna M (2004) Tardiness minimization in a flexible job shop: a tabu search approach. Intelligent Journal of Advance Manufacturing Technology 15(1):103–115

    Google Scholar 

  7. Chen JC, Chen KH, Wu JJ, Chen CW (2008) A study of the flexible job shop scheduling problem with parallel machines and reentrant process. Intelligent Journal of Advance Manufacturing Technology 39(3–4):344–354

    Article  Google Scholar 

  8. Mastrolilli M, Gambardella LM (2000) Effective neighborhood functions for the flexible job shop problem. J Sched 3(1):3–20

    Article  MathSciNet  MATH  Google Scholar 

  9. Saidi-Mehrabad M, Fattahi P (2007) Flexible job shop scheduling with tabu search algorithms. Intelligent Journal of Advance Manufacturing Technology 32(5–6):563–570

    Article  Google Scholar 

  10. Kacem I, Hammadi S, Borne P (2002) Approach by localization multi-objective evolutionary optimization for flexible job-shops scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 32(1):1–13

    Article  Google Scholar 

  11. Kacem I, Hammadi S, Borne P (2002) Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 32(2):172–172

    Article  Google Scholar 

  12. Kacem I, Hammadi S, Borne P (2002) Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computer Simulation 60(3–5):245–276

    Article  MathSciNet  MATH  Google Scholar 

  13. Mati Y, Rezg N, Xie XL (2001) An integrated greedy heuristic for a flexible job shop scheduling problem. Proceedings of the 2001 IEEE International Conference on Systems, Man, and Cybernetics. OPAC, Tucson, pp 2534–2539

  14. Ho NB, Tay JCJ, Lai E (2007) An effective architecture for learning and evolving flexible job-shop schedules. Eur J Oper Res 179:316–333

    Article  MATH  Google Scholar 

  15. Gao J, Gen M, Sun LY, Zhao XH (2007) A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computer and Industrial Engineering 53(1):149–162

    Article  MATH  Google Scholar 

  16. Gao L, Peng CY, Zhou C, Li PG (2006) Solving flexible job-shop scheduling problem using general particle swarm optimization. Proceedings of the 36th International Conference on Computers & Industrial Engineering (ICCIE 2006), Jun 20–23, Taipei, China, pp 3018–3027

  17. Zhang GH, Gao L, Li X, Li P (2008) Variable neighborhood genetic algorithm for the flexible job shop scheduling problems. Proceedings of Intelligent Robotics and Applications (ICIRA 08), LNCS Press, October, pp 503–512. doi:10.1007/978-3-540-88518-4-54

  18. Zhang GH, Shao GH, Li PG, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computer & Industrial Engineering 56:1309–1318

    Article  Google Scholar 

  19. Zhang GH, Gao L, Shi Y (2010) A genetic algorithm and tabu search for multi objective flexible job shop scheduling problems. Proceedings of the 1st International Conference on Computing, Control and Industrial Engineering (CCIE 2010), Wuhan, China, June 5–6, pp 215–254

  20. Zhang GH, Gao L, Shi Y (2010) A novel variable neighborhood genetic algorithm for multi-objective flexible job-shop scheduling problems. Adv Mater Res 118–120:369–373

    Google Scholar 

  21. Zhang GH, Gao L, Shi Y (2011) An effective genetic algorithm for the flexible job-shop scheduling problem. Experts System with Applications 38(4):3563–3573

    Article  Google Scholar 

  22. Wang X, Gao L, Zhang G, Shao X (2010) A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. Int J Adv Manuf Technol 51(5–8):757–767

    Article  Google Scholar 

  23. Wallace A (2005) The geographical distribution of animals (two volumes). Adamant Media Corporation, Boston

    Google Scholar 

  24. Darwin C (1995) The origin of species. Gramercy, New York

    Google Scholar 

  25. MacArthur R, Wilson E (1967) The theory of biogeography. Princeton University Press, Princeton

    Google Scholar 

  26. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713

    Article  Google Scholar 

  27. Simon D (2009) A probabilistic analysis of a simplified biogeography-based optimization algorithm. http://academic.csuohio.edu/simond/bbo/simplified/bbosimplified.pdf

  28. Du D, Simon D, Ergezer M (2009) Biogeography-based optimization combined with evolutionary strategy and immigration refusal. IEEE International Conference on Systems, Man, and Cybernetics. San Antonio, TX, pp 1023–1028

  29. Ergezer M, Simon D, Du DW (2009) Oppositional biogeography-based optimization. IEEE Conference on Systems, Man, and Cybernetics, San Antonio, TX, pp 1035–1040

  30. Ma H, Chen X (2009) Equilibrium species counts and migration model tradeoffs for biogeography-based optimization. 48th IEEE Conference on Decision and Control

  31. Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Intell 24:517–525. doi:10.1016/j.engappai.2010.08.005

    Article  Google Scholar 

  32. Panchal V, Singh P, Kaur N, Kundra H (2009) Biogeography based satellite image classification. Int J Comp Sci Inform Secur 6(2):269–274

    Google Scholar 

  33. Kundra H, Kaur A, Panchal V (2009) An integrated approach to biogeography based optimization with case based reasoning for retrieving groundwater possibility. Proceedings of the Eighth Annual Asian Conference and Exhibition on Geospatial Information, Technology and Applications, August 2009, Singapore

  34. Bhattacharya A, Chattopadhyay PK (2010) Solving complex economic load dispatch problems using biogeography-based optimization. Expert Systems with Applications 37:3605–3615

    Article  Google Scholar 

  35. Zhang CY, Rao YQ, Li PG, Shao XY (2007) Bilevel genetic algorithm for the flexible job-shop scheduling problem. Jixie Gongcheng Xuebao/Chin J Mech Eng 43(4):119–124 (in Chinese)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Zandieh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rahmati, S.H.A., Zandieh, M. A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. Int J Adv Manuf Technol 58, 1115–1129 (2012). https://doi.org/10.1007/s00170-011-3437-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3437-9

Keywords

Navigation