Numerical Methods for Solving Discrete Event Systems: With Applications to Queueing Systems

This graduate textbook offers an alternative to discrete event simulation by formulating discrete event systems, converting them into Markov chains, and calculating transient and equilibrium probabilities. It features probabilistic interpretation of Gaussian elimination and templates for efficient algorithms, making it useful for practitioners and teaching stochastic processes courses.

Format: Hardback
Length: 362 pages
Publication date: 10 September 2022
Publisher: Springer International Publishing AG

This comprehensive graduate textbook presents a novel alternative to discrete event simulation. It delves into the process of formulating discrete event systems, transforming them into Markov chains, and calculating their transient and equilibrium probabilities. Extensive guidance is provided on the most suitable methods for obtaining these probabilities, along with templates for efficient algorithms that can be executed on even laptops with hundreds of thousands of states. Moreover, this book showcases the probabilistic interpretation of Gaussian elimination, a concept that unifies various topics covered, including embedded Markov chains and matrix analytic methods. The material presented in this book is highly valuable for practitioners seeking to solve complex problems. Additionally, the book offers an engaging approach to teaching courses on stochastic processes, making it an essential resource for students and scholars in the field.

This comprehensive graduate textbook presents a novel alternative to discrete event simulation. It delves into the process of formulating discrete event systems, transforming them into Markov chains, and calculating their transient and equilibrium probabilities. Extensive guidance is provided on the most suitable methods for obtaining these probabilities, along with templates for efficient algorithms that can be executed on even laptops with hundreds of thousands of states. Moreover, this book showcases the probabilistic interpretation of Gaussian elimination, a concept that unifies various topics covered, including embedded Markov chains and matrix analytic methods. The material presented in this book is highly valuable for practitioners seeking to solve complex problems. Additionally, the book offers an engaging approach to teaching courses on stochastic processes, making it an essential resource for students and scholars in the field.

Weight: 774g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031100819
Edition number: 1st ed. 2022