Skip to main content
SpringerLink
Log in

A Hybrid Genetic Algorithm and Tabu Search for Multi-objective Dynamic JSP

  • Chapter
  • First Online:
Effective Methods for Integrated Process Planning and Scheduling

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 2))

Abstract

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this chapter, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the real-time events are difficult to be expressed and taken into account by the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are schedule efficiency and schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has been compared with some common dispatching rules and meta-heuristic algorithms which have widely been used in the literature. The experimental results illustrate that the proposed method is very effective in various shop floor conditions.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
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. Abawajy JH (2005) Fault-tolerant dynamic job scheduling policy. Lect Notes Comput Sci 3719(2005):165–173

    Article  Google Scholar 

  2. Adibi MA, Zandieh M, Amiri M (2010) Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Syst Appl 37(1):282–287

    Article  Google Scholar 

  3. Alpay S, Yuzugullu N (2009) Dynamic job shop scheduling for missed due date performance. Int J Prod Res 47(15):4047–4062

    Article  Google Scholar 

  4. Aytug H, Lawley MA, Mckay K, Mohan S (2005) Executing production schedules in the face of uncertainties: a review and some future directions. Eur J Oper Res 161(1):86–110

    Article  MathSciNet  Google Scholar 

  5. Chryssolouris G, Subramaniam V (2001) Dynamic scheduling of manufacturing job shops using genetic algorithms. J Intell Manuf 12(3):281–293

    Article  Google Scholar 

  6. Cowling PI, Johansson M (2002) Using real-time information for effective dynamic scheduling. Eur J Oper Res 139(2):230–244

    Article  MathSciNet  Google Scholar 

  7. Dewan P, Joshi S (2002) Auction-based distributed scheduling in a dynamic job shop environment. Int J Prod Res 40(5):1173–1191

    Article  Google Scholar 

  8. Dominic PDD, Kaliyamoorthy S, Kumar MS (2004) Efficient dispatching rules for dynamic job shop scheduling. Int J Adv Manuf Technol 24:70–75

    Google Scholar 

  9. Gao L, Zhang GH, Zhang LP, Li XY (2011) An efficient memetic algorithm for solving the job shop scheduling problem. Comput Ind Eng 60:699–705

    Article  Google Scholar 

  10. Goren S, Sabuncuoglu I (2010) Optimization of schedule robustness or stability under random machine breakdowns and processing time variability. IIE Trans 42(3):203–220

    Article  Google Scholar 

  11. Holloway CA, Nelson RT (1974) Job shop scheduling with due dates and variable processing times. Manag Sci 20:1264–1275

    Article  Google Scholar 

  12. Kashan AH, Karimi B, Jolai F (2010) An effective hybrid multi-objective genetic algorithm for bi-criteria scheduling on a single batch processing machine with non-identical job sizes. Eng Appl Artif Intell 23:911–922

    Article  Google Scholar 

  13. Kutanoglu E, Sabuncuoglu I (2001) Routing-based reactive scheduling policies for machine failures in dynamic job shops’. Int J Prod Res 39(14):3141–3158

    Article  Google Scholar 

  14. Leitão P (2011) A holonic disturbance management architecture for flexible manufacturing systems. Int J Prod Res 49(5):1269–1284

    Article  Google Scholar 

  15. Liu SQ, Ong HL, Ng KM (2005) Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem. Adv Eng Softw 36:199–205

    Article  Google Scholar 

  16. Malve S, Uzsoy R (2007) A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Comput Oper Res 34:3016–3028

    Article  MathSciNet  Google Scholar 

  17. Nie L, Shao XY, Gao L, Li W (2010) Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int J Adv Manuf Technol 50:729–747

    Article  Google Scholar 

  18. Nowicki E, Smutnicki C (1996) A fast taboo search algorithm for the job shop problem. Manag Sci 42(6):797–813

    Article  Google Scholar 

  19. Kouiss K, Pierreval H, Mebarki N (1997) Using multi-agent architecture in FMS for dynamic scheduling. J Intell Manuf 8:41–47

    Article  Google Scholar 

  20. Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12:417–431

    Article  MathSciNet  Google Scholar 

  21. Pierreval H, Mebarki N (1997) Dynamic scheduling selection of dispatching rules for manufacturing system. Int J Prod Res 35(6):1575–1591

    Article  Google Scholar 

  22. Pinedo M (2002) Scheduling theory, algorithms, and systems. Prentice-Hall, Englewood Cliffs, NJ (Chapter 2)

    MATH  Google Scholar 

  23. Rajendran C, Holthaus O (1999) A comparative study of dispatching rules in dynamic flowshops and jobshops. Eur J Oper Res 116:156–170

    Article  Google Scholar 

  24. Rangsaritratsamee R, Ferrell WG Jr, Kurz BM (2004) Dynamic rescheduling that simultaneously considers efficiency and stability. Comput Ind Eng 46:1–15

    Article  Google Scholar 

  25. Renna P (2010) Job shop scheduling by pheromone approach in a dynamic environment. Int J Comput Integr Manuf 23(5):412–424

    Article  Google Scholar 

  26. Sabuncuoglu I, Goren S (2009) Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. Int J Comput Integr Manuf 22:138–157

    Article  Google Scholar 

  27. Sabuncuoglu I, Kizilisik B (2003) Reactive scheduling in a dynamic and stochastic FMS environment. Int J Prod Res 41(17):4211–4231

    Article  Google Scholar 

  28. Shafaei R, Brunn P (1999) Workshop scheduling using practical (inaccurate) data Part 1: the performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach. Int J Prod Res 37(17):3913–3925

    Article  Google Scholar 

  29. Subramaniam V, Lee GK, Ramesh T, Hong GS, Wong YS (2000) Machine selection rules in a dynamic job shop. Int J Adv Manuf Technol 16:902–908

    Article  Google Scholar 

  30. Vieira GE, Herrmann JW, Lin E (2003) Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J Sched 6:39–62

    Article  MathSciNet  Google Scholar 

  31. Vinod V, Sridharan R (2009) Simulation-based metamodels for scheduling a dynamic job shop with sequence-dependent setup times. Int J Prod Res 47(6):1425–1447

    Article  Google Scholar 

  32. Wang L, Zheng DZ (2001) An effective hybrid optimization strategy for job-shop scheduling problems. Comput Oper Res 28:585–590

    Article  MathSciNet  Google Scholar 

  33. Xiang W, Lee HP (2008) Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng Appl Artif Intell 21:73–85

    Article  Google Scholar 

  34. Zandieh M, Adibi MA (2010) Dynamic job shop scheduling using variable neighbourhood search. Int J Prod Res 48(8):2449–2459

    Article  Google Scholar 

  35. Zhang CY, Li PG, Guan ZL, Rao YQ (2007) A tabu search algorithm with a new neighbourhood structure for the job shop scheduling problem. Comput Oper Res 34(11):3229–3242

    Article  MathSciNet  Google Scholar 

  36. Zhang CY, Rao YQ, Li PG (2008) An effective hybrid genetic algorithm for the job shop scheduling problem. Int J Adv Manuf Technol 39:965–974

    Article  Google Scholar 

  37. Zhou R, Nee AYC, Lee HP (2009) Performance of an ant colony optimization algorithm in dynamic job shop scheduling problems. Int J Prod Res 47(11):2903–2920

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Xinyu Li

  2. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Liang Gao

Authors
  1. Xinyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyu Li .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer-Verlag GmbH Germany, part of Springer Nature and Science Press, Beijing

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, X., Gao, L. (2020). A Hybrid Genetic Algorithm and Tabu Search for Multi-objective Dynamic JSP. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-55305-3_18

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-55303-9

  • Online ISBN: 978-3-662-55305-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation