Srinivas R. Chakravarthy 
Introduction to Matrix-Analytic Methods in Queues 2 [PDF ebook] 
Analytical and Simulation Approach – Queues and Simulation

Soporte
Matrix-analytic methods (MAM) were introduced by Professor Marcel Neuts and have been applied to a variety of stochastic models since.

In order to provide a clear and deep understanding of MAM while showing their power, this book presents MAM concepts and explains the results using a number of worked-out examples. This book’s approach will inform and kindle the interest of researchers attracted to this fertile field.

To allow readers to practice and gain experience in the algorithmic and computational procedures of MAM, Introduction to Matrix-Analytic Methods in Queues 2 provides a number of computational exercises. It also incorporates simulation as another tool for studying complex stochastic models, especially when the state space of the underlying stochastic models under analytic study grows exponentially.

This book’s detailed approach will make it more accessible for readers interested in learning about MAM in stochastic models.
€126.99
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Tabla de materias

1. Single-Server Queues Embedded at Departure Epochs

2. Single-Server Queues Embedded at Arrival Epochs

3. Single-Server Queues Based on Arbitrary Epochs

4. Busy Period in Queues

5. Multi-Server Queues

6. Finite-Capacity Queues

7. Simulation

Sobre el autor

Srinivas R. Chakravarthy retired from Kettering University in Michigan, USA after serving as Professor of Mathematics, and as Professor and Head of Industrial and Manufacturing Engineering. He was bestowed the Distinguished Faculty (Kettering’s Faculty and Alumni Honor Wall) award in 2015. He obtained his Ph D under the supervision of Professor Marcel Neuts and is the co-founder of the International Conference Series on MAM in Stochastic Models. His research interests are in queues, inventory and reliability.
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Idioma Inglés ● Formato PDF ● Páginas 448 ● ISBN 9781394174188 ● Tamaño de archivo 41.3 MB ● Editorial John Wiley & Sons ● Publicado 2022 ● Edición 1 ● Descargable 24 meses ● Divisa EUR ● ID 8640630 ● Protección de copia Adobe DRM
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