Skip to main content

Methods for Online Scheduling

  • Chapter
  • First Online:
Online Scheduling in Manufacturing
  • 1008 Accesses

Abstract

This chapter considers how schedules are generated and to be modified in online scheduling environments. We first categorize the online scheduling into two types, dispatching and schedule revisions. Second, we overview the procedure of dispatching and the well-known rules of scheduling, and then discuss the schedule revision to understand that a so-called right-shift operation and an iterative schedule revision are effective to cope with uncertainty caused by dynamic changes in manufacturing environments. We also glance at knowledge-based approaches which have played an important role in online scheduling.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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. Akűrk MS, Gűrel S (1999) Match-up scheduling under a machine breakdown. Eur J Oper Res 112(1):81–97

    Google Scholar 

  2. Aktűrk MS, Atamtűrk A, Gűrel S (2010) Parallel machine match-up scheduling with manufacturing cost considerations. J Sched 13:95–110

    Google Scholar 

  3. Bean JC, Birge JR, Mittenthal J, Noon CE (1991) Matchup scheduling with multiple resources, release dates and disruptions. Oper Res 39(3):470–483

    Google Scholar 

  4. Bierwirth C, Mattfeld DC (1999) Production scheduling and rescheduling with genetic algorithm. Evol Comput 7(1):1–17

    Google Scholar 

  5. Church LK, Uzsoy R (1992) Analysis of periodic and event-driven rescheduling policies in dynamic shops. Int J Comput Integr Manuf 5(3):153–163

    Google Scholar 

  6. Huang YG, Kanal LN, Tripath SK (1990) Reactive scheduling for a single machine: Problem definition, analysis, and heuristic solution. Comput Integr Manuf 3(1):6–12

    Google Scholar 

  7. Jeong KC, Kim YD (1998) A real-time scheduling mechanism for a flexible manufacturing system: using simulation and dispatching rules. Int J Prod Res 36(9):2609–2626

    Google Scholar 

  8. Kerr R, Szelke E (1995) Artificial intelligence in reactive scheduling. Chapman and Hall, London

    Google Scholar 

  9. Mehta SV, Uzsoy RM (1998) Predictable scheduling of a job shop subject to breakdowns. IEEE Trans Robotics Autom 14(3):365–378

    Google Scholar 

  10. Miyashita K, Sycara K (1995) CABINS: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair. Artif Intell 76:377–426

    Google Scholar 

  11. Morton TE, Pentico DW (1993) Heuristic scheduling systems. Wiley, New York

    Google Scholar 

  12. Noronha SJ, Sarma VVS (1991) Knowledge-based approaches for scheduling problems: a survey. IEEE Trans Knowl Data Eng 3(2):160–171

    Google Scholar 

  13. Pinedo M (2008) Scheduling - theory, algorithms, and systems, 3rd edn. Springer, New York

    Google Scholar 

  14. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufman, San Mateo

    Google Scholar 

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

    Google Scholar 

  16. Resende MGC, Sousa JP (2004) Metaheuristics: computer decision making. Kluwer Academic Publishers, London

    Google Scholar 

  17. 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

    Google Scholar 

  18. Shafaei R, Brunn P (1999) Workshop scheduling using practical (inaccurate) data. Part 2: An investigation of the robustness of scheduling rules in a dynamic and stochastic environment. Int J Prod Res 37(18):4105–4117

    Google Scholar 

  19. Shaw MJ, Park S, Raman N (1992) Intelligent scheduling with machine learning capabilities - the induction of scheduling knowledge. IIE Trans 24(2):156–168

    Google Scholar 

  20. Shaw MJ (1998) Introduction to the special issue on information-based manufacturing. Int J Flex Manuf Syst 10:195–196

    Google Scholar 

  21. Smith SF (1995) Reactive scheduling systems. In: Brown DE, Scherer WT (eds) Intelligent scheduling systems. Kluwer Academic Publishers, Boston, pp 155–192

    Google Scholar 

  22. Wu SD, Storer RH, Chang PC (1993) One-machine rescheduling heuristics with efficiency and stability as criteria. Comput Oper Res 20(1):1–14

    Google Scholar 

  23. Yang B, Geunes J (2008) Predictive-reactive scheduling on a single resource with uncertain future jobs. Eur J Oper Res 189:1267–1283

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haruhiko Suwa .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Suwa, H., Sandoh, H. (2013). Methods for Online Scheduling. In: Online Scheduling in Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-4561-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4561-5_4

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4560-8

  • Online ISBN: 978-1-4471-4561-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics