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.
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