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.