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This book bridges theory and application of stochastic programming in operations research. It describes various methods of formulating stochastic optimization problems, and illustrates their advantages and disadvantages with examples and case studies.

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While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental issues are.
The book is easy-to-read, highly illustrated with lots of examples and discussions. It 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.
Stein W. Wallace is a Professor of Operational Research at Lancaster University Management School in England.

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Uncertainty in Optimization.-Modeling Feasibility and Dynamics.-Modeling the Objective Function.- Scenario tree generation, With Michal Kaut.-Service network design, With Arnt-Gunnar Lium and Teodor Gabriel Crainic.- A multi-dimensional newsboy problem with substitution, With Hajnalka Vaagen.- Stochastic Discount Factors.- Long Lead Time Production, With Aliza Heching.- References.- Index

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From the reviews:

"It is the first book that systematically tries to answer the questions about modeling under uncertainty ... . The book is written in a very readable style ... . An experienced researcher who is already familiar with optimization under uncertainty will benefit from reading this book ... ." (Laura Galli, Interfaces, Vol. 43 (5), September-October, 2013)

"The book is intended as a textbook for graduate students and researchers interested in decision making under uncertainty. It is expected that the book will also be suitable for teaching some operations research courses for undergraduates. ... this textbook can indeed be very useful for mathematics students as a methodological guide to the applications of stochastic programming methods. The structure of the textbook is well adapted to teaching purposes." (A. H. Zilinskas, Mathematical Reviews, January, 2013)

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