This area of research is concerned with systems that involve uncertainty. Unlike deterministic systems, a stochastic system does not always generate the same output for a given input. Stochastic systems are represented by stochastic processes that arise in many contexts (e.g., stock prices, patient flows in hospitals, warehouse inventory/stocking processes, and many others).
This area includes:
Queueing Systems: Analysis and design of service systems with uncertainty in the arrival of “customers,” which could include people, materials, or information, and the system’s capacity to process arrivals given the uncertainty in processing time.
Markov Decision Processes: Dynamic and sequential decision making processes that take actions in real-time to achieve optimal performance under conditions of uncertainty and ambiguity about the future.
Reliability and Maintainability: Analysis of systems that are failure-prone and design of systems to achieve high reliability with minimum resources under conditions of risk.