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Process Control with Learning

Author

Listed:
  • Maqbool Dada

    (Purdue University, West Lafayette, Indiana)

  • Richard Marcellus

    (Northern Illinois University, DeKalb, Illinois)

Abstract

We study the control of a production process which moves at a random time from an in-control state to an out-of-control state where an increased number of defective units is produced. After each unit is produced, a decision maker has three choices: continue production, invest in routine maintenance that restores the process to control, and invest in a more expensive learn maintenance that, in addition, may decrease the tendency of the process to go out of control. The optimal policy structure is shown to be of the control-limit type with the property that learning is not optimal if the tendency of the process to go out of control is small enough. If there is no opportunity to inspect the process's output, an optimal policy can be interpreted as a fixed production run. For this case an exact algorithm is developed. When the process's output is inspected, an optimal policy can be interpreted as a random production run. Approximation techniques are presented for this case. The fixed production run model is an alternative technique for determining production lot sixes. The random production run model is an alternative to traditional Shewhart process control.

Suggested Citation

  • Maqbool Dada & Richard Marcellus, 1994. "Process Control with Learning," Operations Research, INFORMS, vol. 42(2), pages 323-336, April.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:2:p:323-336
    DOI: 10.1287/opre.42.2.323
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    Citations

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    Cited by:

    1. Christopher D. Ittner & Venky Nagar & Madhav V. Rajan, 2001. "An Empirical Examination of Dynamic Quality-Based Learning Models," Management Science, INFORMS, vol. 47(4), pages 563-578, April.
    2. Chiel van Oosterom & Lisa M. Maillart & Jeffrey P. Kharoufeh, 2017. "Optimal maintenance policies for a safety‐critical system and its deteriorating sensor," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(5), pages 399-417, August.
    3. Anupam Agrawal & Suresh Muthulingam, 2015. "Does Organizational Forgetting Affect Vendor Quality Performance? An Empirical Investigation," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 350-367, July.
    4. Stephen M. Gilbert & Hena M Bar, 1999. "The value of observing the condition of a deteriorating machine," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(7), pages 790-808, October.
    5. Taylor, W.A. & Wright, G.H., 2006. "The contribution of measurement and information infrastructure to TQM success," Omega, Elsevier, vol. 34(4), pages 372-384, August.
    6. Yimin Wang & Wendell Gilland & Brian Tomlin, 2010. "Mitigating Supply Risk: Dual Sourcing or Process Improvement?," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 489-510, September.
    7. Hao Zhang & Weihua Zhang, 2023. "Analytical Solution to a Partially Observable Machine Maintenance Problem with Obvious Failures," Management Science, INFORMS, vol. 69(7), pages 3993-4015, July.
    8. Narayanan, V. G. & Davila, Antonio, 1998. "Using delegation and control systems to mitigate the trade-off between the performance-evaluation and belief-revision uses of accounting signals," Journal of Accounting and Economics, Elsevier, vol. 25(3), pages 255-282, June.
    9. Amit Shankar Mukherjee & Michael A. Lapré & Luk N. Van Wassenhove, 1998. "Knowledge Driven Quality Improvement," Management Science, INFORMS, vol. 44(11-Part-2), pages 35-49, November.

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