Skip to main content
SpringerLink
Log in

GEP-Based Reactive Scheduling Policies for Dynamic FJSP with Job Release Dates

  • 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

Flexible Job Shop Scheduling Problem (FJSSP) is a generalization of Job Shop Scheduling Problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all jobs to be processed are available at the beginning of the scheduling horizon. The assumption, however, is always violated in practical industries because jobs usually arrive over time and cannot be predicted before their arrivals. In this chapter, Dynamic Flexible Job Shop Scheduling Problem (DFJSSP) with job release dates is studied. A heuristic is proposed to implement reactive scheduling for the dynamic scheduling problem. An approach based on Gene Expression Programming (GEP) is also proposed which automatically constructs reactive scheduling policies for the dynamic scheduling. In order to evaluate the performance of the reactive scheduling policies constructed by the proposed GEP-based approach under a variety of processing conditions, three factors, such as the shop utilization, due date tightness, problem flexibility, are considered in the simulation experiments. The results show that the GEP-based approach can construct more efficient reactive scheduling policies for DFJSSP with job release dates under a big range of processing conditions and performance measures in comparison with previous approaches.

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. Atlan L, Bonnet J, Naillon M (1994) Learning distributed reactive strategies by genetic programming for the general job shop problem. In: Dankel D, Stewan J (eds) Proceedings of the 7th annual Florida artificial intelligence research symposium, 5–6 May 1994 Pensacola Beach, Florida, USA. IEEE Press, Florida

    Google Scholar 

  2. Aytug H, Lawley MA, McKay K, Mohan S, Uzsoy R (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 

  3. Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New York

    Google Scholar 

  4. Blackstone JH, Phillips DT, Hogg GL (1982) A state-of the-art survey of dispatching rules for manufacturing job shop operations. Int J Prod Res 20(1):27–45

    Article  Google Scholar 

  5. Dimopoulos C, Zalzala AMS (2001) Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv Eng Softw 32(6):489–498

    Article  Google Scholar 

  6. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  7. Garey MR, Johnson DS, Sethi R (1976) The complexity of flow shop and job shop scheduling. Math Oper Res 1(2):117–129

    Article  MathSciNet  Google Scholar 

  8. Geiger CD, Uzsoy R, Aytug H (2006) Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J Sched 9(1):7–34

    Article  Google Scholar 

  9. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston

    MATH  Google Scholar 

  10. Hardy Y, Steeb WH (2002) Gene expression programming and one-dimensional chaotic maps. Int J Mod Phys C 13(1):13–24

    Article  MathSciNet  Google Scholar 

  11. Ho NB, Tay JC (2004) GENACE: an efficient cultural algorithm for solving the flexible job-shop problem. In: Proceedings of the congress on evolutionary computation CEC2004, pp 1759–1766

    Google Scholar 

  12. Ho NB, Tay JC, Lai EMK (2007) An effective architecture for learning and evolving flexible job-shop schedules. Eur J Oper Res 179(2):316–333

    Article  Google Scholar 

  13. Holthaus O, Rajendran C (1997) Efficient dispatching rules for scheduling in a job shop. Int J Prod Econ 48(1):87–105

    Article  Google Scholar 

  14. Jain AS, Meeran S (1998) Deterministic job-shop scheduling: past, present and future. Eur J Oper Res 113(2):390–434

    Article  Google Scholar 

  15. Jakobovic D, Budin L (2006) Dynamic scheduling with genetic programming. In: Lecture notes of computer science, vol 3905, pp 73–84

    Google Scholar 

  16. Jensen MT (2003) Generating robust and flexible job shop schedules using genetic algorithms. IEEE Trans Evol Comput 7(3):275–288

    Article  Google Scholar 

  17. Koza JR (2007) Introduction to genetic programming. In: Lipson H (eds) Proceedings of GECCO 2007: genetic and evolutionary computation conference, 7–11 July 2007 London, England, UK. ACM Press, London, pp 3323-3365

    Google Scholar 

  18. Li L, Wang KQ (2009) Multi-objective Flexible Job Shop Schedule Based on Improved Ant Colony Algorithm. In: Proceedings of ICIA2009: international conference on information and automation, 1–3, pp 1158–1162

    Google Scholar 

  19. Miyashita K (2000) Job-shop scheduling with genetic programming. In: Whitley LD, Goldberg DE et al (eds) Proceedings of genetic and evolutionary computation conference (GECCO-2000), 8–12 July 2000 Las Vegas, Nevada, USA. Morgan Kaufmann, San Francisco, CA, USA, pp 505–512

    Google Scholar 

  20. Panwalkar S, Wafik I (1977) A survey of scheduling rules. Oper Res 25(1):45–61

    Article  MathSciNet  Google Scholar 

  21. Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35(10):3202–3212

    Article  Google Scholar 

  22. Pinedo M (1995) Scheduling theory, algorithms, and systems. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  23. Potts CN, Strusevich VA (2009) Fifty years of scheduling: a survey of milestones. J Oper Res Soc 60:S41–S68

    Article  Google Scholar 

  24. Ramasesh R (1990) Dynamic job shop scheduling: a survey of simulation research. Omega 18(1):43–57

    Article  Google Scholar 

  25. Saidi-Mehrabad M, Fattahi P (2007) Flexible job shop scheduling with tabu search algorithms. Int J Adv Manuf Technol 32(5–6):563–570

    Article  Google Scholar 

  26. Tay JC, Ho NB (2008) Evolving dispatching rules using Genetic Programming for solving multi-objective flexible job-shop problem. Comput Ind Eng 54(3):453–473

    Article  Google Scholar 

  27. Vieira GE, Hermann JW, Lin E (2003) Rescheduling manufacturing systems: a framework of strategies, policies and methods. J Sched 6(1):39–62

    Article  MathSciNet  Google Scholar 

  28. Yin WJ, Liu M, Wu C (2003) Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Sarker R et al (eds) Proceeding of CEC2003: congress on evolutionary computation, 9–12 December 2003 Canberra, Australia. IEEE Press, Piscataway, NJ, pp 1050–1055

    Google Scholar 

  29. Zhang GH, Shao XY, Li PG, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309–1318

    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). GEP-Based Reactive Scheduling Policies for Dynamic FJSP with Job Release Dates. 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_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-55305-3_19

  • 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