Alan J. King,Stein W. Wallace
Modeling with Stochastic Programming
Modeling with Stochastic Programming
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Excellent- More about Modeling with Stochastic Programming
This book provides a comprehensive guide to modeling uncertainty in mathematical programming, covering scenario generation and multi-stage models. It is suitable for graduate students and researchers in OR/MS and optimization-oriented engineering disciplines.
Format: Hardback
Length: 202 pages
Publication date: 01 June 2024
Publisher: Springer International Publishing AG
Uncertainty modeling in mathematical programming is a crucial aspect of decision-making under uncertainty. It involves representing random phenomena in the models and analyzing the resulting stochastic models. This text provides a comprehensive overview of uncertainty modeling in mathematical programming, including how to reformulate deterministic models, represent random phenomena, and analyze multi-stage models. The second edition has important extensions regarding how to represent random phenomena in the models and a new chapter on multi-stage models. The book is easy-to-read, highly illustrated with lots of examples and discussions, and will be suitable for graduate students and researchers working in operations research, mathematics, engineering, and related departments where there is interest in learning how to model uncertainty.
Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York, and Stein W. Wallace is a Professor of Operational Research and head of the Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.
Dimension: 235 x 155 (mm)
ISBN-13: 9783031545498
Edition number: 2nd ed. 2024
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