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Effects of operator learning on production output: a Markov chain approach

Author

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  • Corey Kiassat

    (Department of Industrial Engineering, Quinnipiac University, Hamden, CT, USA)

  • Nima Safaei

    (Department of Maintenance Support and Planning, Bombardier Aerospace, Toronto, ON, Canada)

  • Dragan Banjevic

    (Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada)

Abstract

We develop a Markov chain approach to forecast the production output of a human-machine system, while encompassing the effects of operator learning. This approach captures two possible effects of learning: increased production rate and reduced downtime due to human error. In the proposed Markov chain, three scenarios are possible for the machine at each time interval: survival, failure, and repair. To calculate the state transition probabilities, we use a proportional hazards model to calculate the hazard rate, in terms of operator-related factors and machine working age. Given the operator learning curves and their effect on reducing human error over time, the proposed approach is considered to be a non-homogeneous Markov chain. Its result is the expected machine uptime. This quantity, along with production forecasting at various operator skill levels, provides us with the expected production output.

Suggested Citation

  • Corey Kiassat & Nima Safaei & Dragan Banjevic, 2014. "Effects of operator learning on production output: a Markov chain approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(12), pages 1814-1823, December.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:12:p:1814-1823
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