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mAOR: A heuristic-based reactive repair mechanism for job shop schedules

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Abstract

Literature on job shop scheduling has primarily focused on the development of predictive schedules that generate an allocation sequence of jobs on machines. However, in practice, frequent deviations from a predictive schedule occur when the job shop experiences either external (e.g. unexpected arrival of urgent jobs) or internal disturbances (e.g. machine breakdowns) and renders the schedules inefficient. The reactive repair of the original schedule is a better alternative to total rescheduling, as the latter is not only time consuming but also leads to shop floor nervousness. Most of the existing schedule repair heuristics handle singular disruptions only. In this paper, the typical job shop disruptions are studied and their repair processes are decomposed into four generic repair steps, which are achieved using the proposed modified AOR (mAOR) heuristic. An extensive simulation study has also been conducted to evaluate the performance of the mAOR schedule repair heuristic, and the results indicate that the mAOR heuristic is effective in repairing job shop schedules when faced with disruptions.

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References

  1. Szelke E, Kerr RM (1994) Foreword. IFIP Trans B (Applications in Technology) B-15:1

  2. Dorn J (1994) Case based reactive scheduling. In: Artificial intelligence in reactive scheduling. Chapman and Hall, UK, pp 32–50

  3. Abumaizar RJ, Svestka JA (1997) Rescheduling job shops under random disruptions. Int J Prod Res 35(7):2065–2082

    Article  Google Scholar 

  4. Brandimarte P, Rigodanza M, Roero L (2000) Conceptual modeling of an object oriented scheduling architecture based on the shifting bottleneck procedure. IIE Trans 32(10):921–929

    Article  Google Scholar 

  5. Hasle G, Smith SF (1994) Directing an opportunist scheduler: an empirical investigation on reactive scenario. In: Artificial intelligence in reactive scheduling. Chapman and Hall (UK), pp 1–11

  6. Henseler H (1994) From reactive to active scheduling by using multi agents. In: Artificial intelligence in reactive scheduling. Chapman and Hall, UK, pp 12–18

  7. Szelke E, Markus G (1994) A black board based perspective of reactive scheduling. In: Artificial intelligence in reactive scheduling. Chapman and Hall, UK, pp 60–77

  8. Dorn J, Kerr R, Thalhammer G (1994), Reactive scheduling in a fuzzy-temporal framework. Knowledge based reactive scheduling. IFIP Trans B (Applications in Technology) B-15:39–54

    Google Scholar 

  9. Schmidt G (1994) How to apply fuzzy logic to reactive production scheduling. Knowledge based reactive scheduling. IFIP Trans B (Applications in Technology) B-15:57–66

  10. Szelke E., Markus G(1997) A learning reactive scheduler using CBR/L. Comput Ind 33(1):31–46

    Article  Google Scholar 

  11. Miyashita K (1995) Case based knowledge acquisition for schedule optimization. Artif Intell Eng 9(4):277–287

    Article  Google Scholar 

  12. Spargg JE, Fozzard G, Tyler DJ (1997) Constraint based reactive rescheduling in a stochastic environment. Proceedings, recent advances in AI planning. 4th European conference on planning, ECP'97, pp 403–413

  13. Garner BJ, Ridley GJ (1994) Application of neural network process models in reactive scheduling. Knowledge based reactive scheduling. IFIP Trans B (Applications in Technology) B-15:19–28

    Google Scholar 

  14. Rovithakis GA, Perrakis SE, Christodoulou MA (2001) Application of a neural network scheduler on a real manufacturing system. IEEE Con Sy 9(2):261–270

    Article  Google Scholar 

  15. Qi JG, Burns GR, Harrison DK (2000) The application of the parallel multi population genetic algorithms to dynamic job shop scheduling. Int J Adv Manuf Technol 16:609–615

    Google Scholar 

  16. Gershwin SB (1994) Manufacturing systems engineering. Prentice Hall, Englewood Cliffs, NJ

  17. Henseler H (1994) Reaktion: a system for event independent reactive scheduling. In: Artificial intelligence in reactive scheduling. Chapman and Hall, UK, pp 19–31

  18. 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 

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Authors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore, Republic of Singapore

    Velusamy Subramaniam & Amritpal Singh Raheja

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  1. Velusamy Subramaniam
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  2. Amritpal Singh Raheja
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Correspondence to Velusamy Subramaniam.

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Subramaniam, V., Raheja, A.S. mAOR: A heuristic-based reactive repair mechanism for job shop schedules. Int J Adv Manuf Technol 22, 669–680 (2003). https://doi.org/10.1007/s00170-003-1601-6

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  • DOI: https://doi.org/10.1007/s00170-003-1601-6

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