Increasing complexity in manufacturing strategies and swift changes in market and consumer requirements have driven recent studies of manufacturing systems, with transient behavior being identified as a key research area. Till date, satisfying consumer demand has focused on steady-state planning of production, mostly using stochastic or deterministic optimal control methods. Due to the difficulty of obtaining optimal control for many practical situations, as well as in evaluating performance under optimal control, these studies have not been conducive to the analysis or control of transient behavior. This paper bridges this gap by applying model predictive control to a manufacturing system modeled as a discrete-time Markov chain. By modifying the initiation of production as probabilities within the Markov chain, a method is proposed to directly control the system to specific expected performance levels and improve its stochastic transient behavior.
Application of Model Predictive Control to Control Transient Behavior in Stochastic Manufacturing System Models
Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received November 19, 2014; final manuscript received August 25, 2015; published online April 7, 2016. Assoc. Editor: Jianjun Shi.
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Fazlirad, A., and Freiheit, T. (April 7, 2016). "Application of Model Predictive Control to Control Transient Behavior in Stochastic Manufacturing System Models." ASME. J. Manuf. Sci. Eng. August 2016; 138(8): 081007. https://doi.org/10.1115/1.4031497
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