This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields. Unlike most books on the subject, much attention is paid to problems with functional constraints and the realizability of strategies.
Three major methods of investigations are presented, based on dynamic programming, linear programming, and reduction to discrete-time problems. Although the main focus is on models with total (discounted or undiscounted) cost criteria, models with average cost criteria and with impulsive controls are also discussed in depth.
The book is self-contained. A separate chapter is devoted to Markov pure jump processes and the appendices collect the requisite background on real analysis and applied probability. All the statements in the main text are proved in detail.
Researchers and graduate students in applied probability, operational research, statistics and engineering will find this monograph interesting, useful and valuable.
About the Author
Alexey Piunovskiy was born in Moscow, USSR, in 1954. He obtained his PhD in 1981 from the Moscow State Institute of Electronics and Mathematics, and his DSc from the Moscow State Institute of Physics and Technology in 2000. Since then he has been at the University of Liverpool, where he is presently a Reader. His research interests include stochastic optimal control and constrained optimization. Yi Zhang was born in Qingdao, PRC, in 1985. He obtained his PhD in 2010 from the Department of Mathematical Sciences at the University of Liverpool, where he is currently a Lecturer. His research interests include stochastic models in operational research, Markov decision processes and their applications to problems in statistics and optimal control.