Skip to main content

Advertisement

SpringerLink
Log in

A parallel genetic algorithm for a flexible job-shop scheduling problem with sequence dependent setups

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The flexible job-shop scheduling problem is an extension of the classical job-shop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. Thus, the problem is to simultaneously assign each operation to a machine (routing problem), prioritize the operations on the machines (sequencing problem), and determine their starting times. The minimization of the maximal completion time of all operations is a widely used objective function in solving this problem. This paper presents a mathematical model for a flexible job-shop scheduling problem incorporating sequence-dependent setup time, attached or detached setup time, machine release dates, and time lag requirements. In order to efficiently solve the developed model, we propose a parallel genetic algorithm that runs on a parallel computing platform. Numerical examples show that parallel computing can greatly improve the computational performance of the algorithm.

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. Allahverdi A, Ng C, Cheng T, Kovalyov M (2008) A survey of scheduling problems with setup times or costs. Eur J Oper Res 187:985–1032

    Article  MATH  MathSciNet  Google Scholar 

  2. Baker K (1974) Introduction to sequence and scheduling. Wiley, New York

    Google Scholar 

  3. Baxter F (1990) Information technology and global changing science. In: Conference on global change: economic issues in agriculture, forestry and natural resources, Washington, DC, 19–21 November 1990

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

    Article  MathSciNet  Google Scholar 

  5. Cantú-Paz E (2000) Efficient and accurate parallel genetic algorithms. Kluwer Academic, Norwell

    MATH  Google Scholar 

  6. Chaudhry SS, Luo W (2005) Application of genetic algorithms in production and operations management: a review. Int J Prod Res 43:4083–4101

    Article  MATH  Google Scholar 

  7. Chen H, Ihlow J, Lehmann C (1999) A genetic algorithm for flexible job-shop scheduling. In: The proceedings of the 1999 IEEE international conference on robotics & automation, Detroit, pp 1120–1125

  8. Chen J, Chen K, Wu J, Chen C (2007) A study of the flexible job shop scheduling problem with parallel machines and reentrant process. Int J Adv Manuf Technol. doi:10.1007/s00170-007-1227-1

    Google Scholar 

  9. Conway R, Maxwell W (1967) Theory of scheduling. Addison-Wesley, Reading

    MATH  Google Scholar 

  10. Defersha FM, Chen M (2007) A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. Int J Prod Res. doi:10.1080/00207540701441962

    Google Scholar 

  11. Gao J, Gen M, Sun L, Zhao X (2007) A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Comput Ind Eng 53:149–162

    Article  MATH  Google Scholar 

  12. Gao J, He G, Wang Y (2009) A new parallel genetic algorithm for solving multiobjective scheduling problems subjected to special process constraint. Int J Adv Manuf Technol 43:151–160

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  14. Garey MR, Johnson DS, Sethi R (1976) The complexity of flowshop and jobshop scheduling. Math Oper Res 1:117–129

    Article  MATH  MathSciNet  Google Scholar 

  15. Guo Z, Wong W, Leug S (2008) A genetic-algorithm-based optimization model for scheduling flexible assembly lines. Int J Adv Manuf Technol 36:156–168

    Article  Google Scholar 

  16. Guo Z, Wong W, Leung S, Fan J, Chen S (2008) A genetic algorithm based optimization model for solving the flexible assembly line balancing problem with work-sharing and workstation revisiting. IEEE Trans Syst Man Cybern, Part C Appl Rev 38:218–228

    Article  Google Scholar 

  17. Guo Z, Wong W, Leung S, Fan J, Chen S (2008) Genetic optimization of order scheduling with multiple uncertainties. Expert Syst Appl 35:1788–1801

    Article  Google Scholar 

  18. Guo Z, Wong W, Leung S, Fan J, Chan SF (2006) Mathematical model and optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: a case study based on the apparel industry. Comput Ind Eng 50:202–219

    Article  Google Scholar 

  19. Gupta J (1986) Flowshop schedules with sequence dependent setup times. J Oper Res Soc Jpn 29:206–219

    MATH  Google Scholar 

  20. ILOG Inc (2008) CPLEX 12.0 user’s manual. 1080 Linda Vista Ave. Mountain View, CA 94043. http://www.ilog.com

  21. Jain AS, Meeran S (1998) Deterministic job-shop scheduling: past, present and future. Eur J Oper Res 113:390–434

    Article  Google Scholar 

  22. Jolai F, Sheikh S, Rabbani M, Karimi R (2009) A genetic algorithm for solving no-wait flexible flow lines with due window and job rejection. Int J Adv Manuf Technol 42:523–532

    Article  Google Scholar 

  23. Kacem I, Hammadi S, Borne P (2002) Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans Syst Man Cybern 32:1–3

    Article  Google Scholar 

  24. Kochhar S, Morris R (1987) Heuristic methods for flexible flow line scheduling. J Manuf Syst 6:299–314

    Article  Google Scholar 

  25. Lee K, Yamakawa T, Lee K (1998) A genetic algorithm for general machine scheduling problems. International Journal of Knowledge-Based Electronic 2:60–66

    Google Scholar 

  26. Manikas A, Chang Y (2008) Multi-criteria sequence-dependent job shop scheduling using genetic algorithms. Comput Ind Eng 56:179–185

    Article  Google Scholar 

  27. Osman I, Potts C (1989) Simulated annealing for permutation flow-shop scheduling. Omega 17:551–557

    Article  Google Scholar 

  28. Panwalkar SS, Dudek RA, Smith ML (1973) Sequencing research and the industrial scheduling problem. In: Elmaghraby SE (ed) Symposium on the theory of scheduling and its applications. Springer, New York, p 29

    Google Scholar 

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

    Article  MATH  Google Scholar 

  30. Reeves CR (1995) A genetic algorithm for flowshop sequencing. Comput Oper Res 22:5–13

    Article  MATH  Google Scholar 

  31. Rios-Mercado R, Bard J (1999) A branch-and-bound algorithm for permutation flow shops with sequence-dependent setup times. IIE Trans 31:721–731

    Google Scholar 

  32. Ruiz R, Şerifoglub FS, Urlings T (2008) Modeling realistic hybrid flexible flowshop scheduling problems. Comput Oper Res 35:1151–1175

    Article  MATH  Google Scholar 

  33. Ruiz R, Maroto C, Alcaraz J (2005) Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics. Eur J Oper Res 165:34–54

    Article  MATH  MathSciNet  Google Scholar 

  34. Saidi M, Fattahi P (2007) Flexible job shop scheduling with tabu search algorithm. Int J Adv Manuf Technol 35:563–570

    Article  Google Scholar 

  35. Snir M, Otto H-L, S S, Walker D, Dongarra J (1998) MPI-the complete reference, vol 1. MPI core, 2nd edn. Scientific and engineering computation series. MIT, Cambridge

    Google Scholar 

  36. Tsai J, Liu T, Ho W, Chou J (2008) An improved genetic algorithm for job-shop scheduling problems using taguchi-based crossover. Int J Adv Manuf Technol 38:987–994

    Article  Google Scholar 

  37. Wang YM, Xiao N, Yin H, Hu E, Zhao C, Jiang Y (2008) A two-stage genetic algorithm for large size job shop scheduling problems. Int J Adv Manuf Technol 39:813–820

    Article  Google Scholar 

  38. Wortman DB (1992) Managing capacity: getting the most from your company’s asset. Ind Eng 24:47–49

    Google Scholar 

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

    Article  Google Scholar 

  40. Xing L, Chen Y, Yang K (2008) Multi-objective flexible job shop schedule: design and evaluation by simulation modeling. Applied Soft Computing. doi:10.1016/j.asoc.2008.04.013

    Google Scholar 

  41. Zhang C, Rao Y, Li P (2008) An effective hybrid genetic algorithm for the job shop scheduling problem. Int J Adv Manuf Technol 39:965–974

    Article  Google Scholar 

  42. Zhang H, Gu M (2008) Modeling job shop scheduling with batches and setup times by timed petri nets. Math Comput Model. doi:10.1016/j.mcm.2008.03.010

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve W., Montreal, Quebec, Canada, H3G 1M8

    Fantahun M. Defersha & Mingyuan Chen

Authors
  1. Fantahun M. Defersha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingyuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingyuan Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Defersha, F.M., Chen, M. A parallel genetic algorithm for a flexible job-shop scheduling problem with sequence dependent setups. Int J Adv Manuf Technol 49, 263–279 (2010). https://doi.org/10.1007/s00170-009-2388-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-2388-x

Keywords

Search

Navigation