Markov Chains and Decision Processes for Engineers and Managers

Recognized as a robust system for dealing with uncertainty, Markov modeling can enhance your ability to analyze advanced manufacturing and service strategies. However, most books on Markov chains or decision processes are typically either highly theoretical, with few examples, or extremely prescriptive, with little justification for the steps of the algorithms used to solve Markov patterns. Offering a unified treatment of Markov series and Markov decision processes in a single volume, Markov Series and Resolution Systems for Engineers and Managers offers a highly detailed description of the construction and solution of Markov patterns that facilitates their application to various processes.

Organized around Markov series construction, the text begins with descriptions of Markov series states, transitions, construction, and patterns, and then discusses general state distributions and transition to a target state in a regular Markov series. The author handles canonical forms and transition to target states or to categories of target states for reducible Markov series. He provides an economic dimension by associating rewards with states, thereby linking a Markov series to a Markov decision system, and then offers opportunities to create a Markov decision system, enabling an analyst to choose among various Markov series with rewards in order to maximize expected rewards. An introduction to state reduction and hidden Markov series completes the coverage.

In a presentation that balances algorithms and applications, the author presents explanations of the logical relationships that underlie the formulation or algorithms through informal derivations, and devotes considerable attention to the construction of Markov patterns. He constructs simplified Markov patterns for a wide array of processes such as the climate, gaming, diffusion of gases, a waiting line, inventory, component selection, machine maintenance, selling a stock, a cost account, a career path, patient flow