Effects of operator learning on production output: a Markov chain approach |
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Authors: | Corey Kiassat Nima Safaei Dragan Banjevic |
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Affiliation: | 1.Department of Industrial Engineering,Quinnipiac University,Hamden, CT,USA;2.Department of Maintenance Support and Planning,Bombardier Aerospace,Toronto, ON,Canada;3.Department of Mechanical & Industrial Engineering,University of Toronto,Toronto, ON,Canada |
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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. |
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