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A Markovian model for process setup and improvement

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  • Linguo Gong
  • James Pruett
  • Kwei Tang

Abstract

We examine the setup and improvement policies for a production process with multiple performance states. Assume that the production process deteriorates randomly over time, following a Markovian process with known transition probabilities. In order to reduce the production cost incurred because of process deterioration, the process is inspected at the end of each period. Then one of three actions may be taken: do nothing, perform routine process setup, or perform routine setup and process improvement. The routine setup operation returns the process to its best performance state, whereas the process improvement action may reduce future production and setup costs and improve the process‐state transition probabilities. A discounted Markovian model is formulated to find the strategy that minimizes the total cost of operating the production process. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 383–400, 1997

Suggested Citation

  • Linguo Gong & James Pruett & Kwei Tang, 1997. "A Markovian model for process setup and improvement," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(4), pages 383-400, June.
    • Handle: RePEc:wly:navres:v:44:y:1997:i:4:p:383-400
    DOI: 10.1002/(SICI)1520-6750(199706)44:43.0.CO;2-2
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    References listed on IDEAS

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    1. Richard L. Marcellus & Maqbool Dada, 1991. "Interactive Process Quality Improvement," Management Science, INFORMS, vol. 37(11), pages 1365-1376, November.
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    6. Evan L. Porteus, 1986. "Optimal Lot Sizing, Process Quality Improvement and Setup Cost Reduction," Operations Research, INFORMS, vol. 34(1), pages 137-144, February.
    7. Peter Kolesar, 1966. "Minimum Cost Replacement Under Markovian Deterioration," Management Science, INFORMS, vol. 12(9), pages 694-706, May.
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    Cited by:

    1. Diaby, Moustapha & Cruz, Jose M. & Nsakanda, Aaron L., 2013. "Shortening cycle times in multi-product, capacitated production environments through quality level improvements and setup reduction," European Journal of Operational Research, Elsevier, vol. 228(3), pages 526-535.
    2. Nobuyuki Tamura, 2018. "Optimal control of production with improvement," Journal of Risk and Reliability, , vol. 232(6), pages 777-785, December.
    3. Williams, William W. & Tang, Kwei & Gong, Linguo, 2000. "Process improvement for a container-filling process with random shifts," International Journal of Production Economics, Elsevier, vol. 66(1), pages 23-31, June.

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