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Proceedings of the 40th IEEE Conference on Decision and Control Orlando, Florida USA, December 2001 WeM13-3 0-7803-7061-9×01/$10.00 2001 IEEE 2108 2109
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Controlled Markov Chains refers to a mathematical model in the field of stochastic processes that incorporates both random elements and decision-making in a sequential manner.
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Researchers, analysts, and practitioners in fields such as operations research, engineering, and computer science may be required to work with or file controlled Markov Chains.
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Controlled Markov Chains can be filled out using mathematical notation and algorithms to model the system dynamics and decision-making processes.
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The purpose of using controlled Markov Chains is to analyze and optimize systems that involve random events and decision-making, such as queueing systems, supply chains, and manufacturing processes.
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Information such as transition probabilities, decision rules, rewards/costs, and system states must be reported when working with controlled Markov Chains.
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