MDPs have been applied in many areas, such as communications, signal processing, artificial intelligence, stochastic scheduling and manufacturing systems, discrete event systems, management and economies. This book examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents three main topics: a new methodology for MDPs with discounted total reward criterion; transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; application of MDPs in stochastic environments, which greatly extends the area where MDPs can be applied. Each topic is used to study optimal control problems or other types of problems.

Presents new branches for Markov Decision Processes (MDP) Applies new methodology for MDPs with discounted total reward criterion Offers new applications of MDPs in areas such as the control of discrete event systems and the optimal allocations in sequential online auctions Shows the validity of the optimality equation and its properties from the definition of the model by reducing the scale of MDP models based on action reduction and state decomposition Presents two new optimal control problems for discrete event systems Examines two optimal replacement problems in stochastic environments Studies continuous time MDPs and semi-Markov decision processes in a semi-Markov environment

Inhalt
Discretetimemarkovdecisionprocesses: Total Reward.- Discretetimemarkovdecisionprocesses: Average Criterion.- Continuous Time Markov Decision Processes.- Semi-Markov Decision Processes.- Markovdecisionprocessesinsemi-Markov Environments.- Optimal control of discrete event systems: I.- Optimal control of discrete event systems: II.- Optimal replacement under stochastic Environments.- Optimalal location in sequential online Auctions.