A Method for Stationary Analysis and Control during Transience in Multi-State Stochastic Manufacturing Systems.

摘要

Ever-increasing complexity and swift changes in market and consumer requirements have become the defining traits of manufacturing in our modern day. In response to these features, the study of manufacturing requires models and control strategies that reflect this complexity and accommodate market changes and consumer needs. As a result, performance analysis and control of complicated stochastic manufacturing systems, and the investigation of their transient behavior---identified as a key research area, are in need of further in depth study.;This thesis investigates the design and operation of manufacturing systems by adopting a Markov chain model for stochastic multi-state manufacturing systems. Different applications of the model, including a supply and demand system, are introduced and a methodology is developed and verified that determines the solution for its steady-state performance.;Methods proposed to date for stochastic or deterministic optimal control of manufacturing systems are not conducive to the analysis or control of transient behaviour, nor to study of their transient behavior. Applying model predictive control techniques directly to the Markov chain supply and demand model are shown to be a viable alternative for control and study of transient behavior. By modifying the initiation of production as probabilities within the Markov chain, the system was shown to be controllable to specific expected performance levels during transient operation. Taking advantage of the features of Markov chains, detailed analysis has shown that control improves the transient behavior of the system.