Match-Up Strategies for Job Shop Rescheduling

  • Patrick Moratori
  • Sanja Petrovic
  • Antonio Vázquez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)


We investigate the problem of integrating new rush orders into the current schedule of a real world job shop floor. Satisfactory rescheduling methods must keep stability of the shop, by introducing the fewest number of changes in the ordering of operations, while maintaining the same levels of the schedule performance criteria. This paper introduces a number of match-up strategies that modify only part of the schedule in order to accommodate new arriving jobs. These strategies are compared with the right-shift and the total-rescheduling methods, which are optimal regarding stability and performance, but ineffective for performance and stability, respectively. Statistical analysis indicates that the match-up strategies are comparable to right-shift for stability, and as good as total-rescheduling for performance.


Schedule Problem Completion Time Parallel Machine Idle Time Machine Breakdown 
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  1. 1.
    Vieira, G., Herrmann, J., Lin, E.: Rescheduling manufacturing systems: A framework of strategies, policies and methods. Journal of Scheduling 6(1), 39–62 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Aytug, H., Lawley, M., McKay, K., Mohan, S., Uzsoy, R.: Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research 161(1), 86–110 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Bean, J., Birge, J.: Match-up real-time scheduling. In: Proceedings of the Symposium on Real-Time Optimization in Automated Manufacturing Facilities, National Bureau of Standards, Special Publication 724, pp. 197–212 (1986)Google Scholar
  4. 4.
    Bean, J., Birge, J., Mittenthal, J., Noon, C.: Matchup scheduling with multiple resources, release dates and disruptions. Operations Research 39(3), 470–483 (1991)zbMATHCrossRefGoogle Scholar
  5. 5.
    Birge, J., Dempster, M.: Optimality conditions for match-up strategies in stochastic scheduling. Technical Report 92 - 58, Department of Industrial and Operations Engineering, The University of Michigan (1992)Google Scholar
  6. 6.
    Birge, J., Dempster, M.: Optimal match-up strategies in stochastic scheduling. Discrete Applied Mathematics 57(2-3), 105–120 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Akturk, M., Gorgulu, E.: Match-up scheduling under a machine breakdown. European Journal of Operational Research 112(1), 81–97 (1999)zbMATHCrossRefGoogle Scholar
  8. 8.
    Smith, S., Muscettola, N., Matthys, D., Ow, P., Potvin, J.: Opis: an opportunistic factory scheduling system. In: IEA/AIE 1990: Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems, pp. 268–274. ACM Press, New York (1990)CrossRefGoogle Scholar
  9. 9.
    Sadeh, N.: Micro-opportunistic scheduling: The micro-boss factory scheduler. In: Intelligent Scheduling, ch. 4, pp. 99–136. Morgan Kaufmann, San Francisco (1994)Google Scholar
  10. 10.
    Smith, S.: Reactive scheduling systems. In: Brown, D., Scherer, W. (eds.) Intelligent Scheduling Systems, Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
  11. 11.
    Sun, J., Xue, D.: A dynamic reactive scheduling mechanism for responding to changes of production orders and manufacturing resources. Computers in Industry 46(2), 189–207 (2001)CrossRefGoogle Scholar
  12. 12.
    Abumaizar, R., Svestka, J.: Rescheduling job shops under random disruptions. International Journal of Production Research 35(7), 2065–2082 (1997)zbMATHCrossRefGoogle Scholar
  13. 13.
    Wu, S., Storer, R., Chang, P.: One-machine rescheduling heuristics with efficiency and stability as criteria. Computers and Operations Research 20(1), 1–14 (1993)zbMATHCrossRefGoogle Scholar
  14. 14.
    Petrovic, S., Fayad, C., Petrovic, D., Burke, E., Kendall, G.: Fuzzy job shop scheduling with lot-sizing. Annals of Operations Research 159(1), 275–292 (2008)CrossRefMathSciNetzbMATHGoogle Scholar
  15. 15.
    Miller, R.: Simultaneous Statistical Inference. Springer, New York (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Patrick Moratori
    • 1
  • Sanja Petrovic
    • 1
  • Antonio Vázquez
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK

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