Industrial engineering has expanded from its origins in manufacturing to transportation, health care, logistics, services, and more. A common denominator among all these industries, and one of the biggest challenges facing decision-makers, is the unpredictability of systems. Probability Models in Operations Research provides a comprehensive overview of the probabilistic and stochastic modeling approaches commonly used to capture the randomness in industrial and systems engineering.
Balancing applications with mathematical rigor, Probability Models in Operations Research provides an introduction to the use of probability to define models that have applications in industrial engineering and operations research. With a concise review of the key components of probability theory, this book offers coverage of all basic model forms, probability modeling fundamentals, random variables analysis, multiple random variables analysis, Bernoulli processes, discrete-time Markov chains, Poisson processes, renewal processes, continuous-time Markov chains, and queueing theory. The text serves as a practical reference for those who use probability to model system behaviors.
