Modeling Random Processes for Engineers and Managers

Solberg James J.

ISBN 10: 0470322551 / ISBN 13: 9780470322550

Published by John Wiley & Sons, 2008

About this Item

pp. xiii + 304 Illus.

Seller Inventory # 7491238

Bibliographic Details

• Title: Modeling Random Processes for Engineers and Managers
• Publisher: John Wiley & Sons
• Publication Date: 2008
• Language: English
• ISBN 10: 0470322551
• ISBN 13: 9780470322550
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Modeling Random Processes for Engineers and Managers provides students with a "gentle" introduction to stochastic processes, emphasizing full explanations and many examples rather than formal mathematical theorems and proofs. The text offers an accessible entry into a very useful and versatile set of tools for dealing with uncertainty and variation. Many practical examples of models, as well as complete explanations of the thought process required to create them, motivate the presentation of the computational methods. In addition, the text contains a previously unpublished computational approach to solving many of the equations that occur in Markov processes. Modeling Random Processes is intended to serve as an introduction, but more advanced students can use the case studies and problems to expand their understanding of practical uses of the theory.

About the Author

Dr. James J Solberg is the Ransburg Professor of Manufacturing and Industrial Engineering and Director of the Purdue University Center for Collaborative Manufacturing (National Science Foundation Engineering Research Center). Dr. Solberg is the U.S. Regional Coordinator for the International Joint Research Committee on Metamorphic Material Handling Systems is a member of the Executive Committee of the Material Handling Engineering Division of ASME, the chair of the Technical Committee on Material Transport Systems. He also currently serves on the Editorial Board, International Journal of Production Economics. His research interests include manufacturing systems, stochastic models, and queuing networks.