DISCRETE STOCHASTIC MODELS AND APPLICATIONS FOR RELIABILITY ENGINEERING AND STATISTICAL QUALITY CONTROL (HB 2023)

Discrete stochastic models are tools that allow us to understand, control, and optimize engineering systems and processes. This book provides real-life examples and illustrations of models in reliability engineering and statistical quality control and establishes a connection between the theoretical framework and their engineering applications.

The book describes discrete stochastic models along with real-life examples and explores not only well-known models, but also comparatively lesser known ones. It includes definitions, concepts, and methods with a clear understanding of their use in reliability engineering and statistical quality control fields. Also covered are the recent advances and established connections between the theoretical framework of discrete stochastic models and their engineering applications.

An ideal reference for researchers in academia and graduate students working in the fields of operations research, reliability engineering, quality control, and probability and statistics.

Serkan Eryilmaz completed his Ph.D. at Ankara University (Turkey) in 2002. He is currently working as a professor at Department of Industrial Engineering and serving as vice president for research at Atilim University, Ankara. He is the present co-chair of the System Reliability Technical Committee of the European Safety and Reliability Association. He currently serves as a member of the Editorial Board of Reliability Engineering and System Safety and an Area Editor of the IISE Transactions- Quality & Reliability Engineering. His research interests include reliability engineering, applied probability, stochastic modeling and renewable energy systems. He is the author of more than 180 publications. He was honored by TUBITAK (Scientific and Technological Research Council of Turkey) Science Incentive Award due to his highly qualified studies on system reliability in the field of stochastic models/ processes in operations research.