Modeling with Stochastic Programming

Author: Alan J. King and Stein W. Wallace Series: Springer Series in Operations Research and Financial Engineering

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Summary

Description

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.

Critic Reviews

“From the reviews: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. ilinskas, Mathematical Reviews, January, 2013)”

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. Žilinskas, Mathematical Reviews, January, 2013)

Back Cover

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.

More on this Book

This book is about modeling stochastic programs - models solved by optimization technology, whose solutions perform well under uncertainty. Major parts of the book are critical discussions about what different modeling paradigms actually mean and what they imply about the choices under consideration. Understanding why stochastic programs are needed, being able to formulate them, and finally, finding out what it is that makes solutions robust, can help find good solutions without actually solving the stochastic programs. Therefore, this book is much more than a book on how to build unsolvable models. Rather, it shows a way forward so that we can potentially benefit from a modeling framework. The book assumes the reader already has basic undergraduate knowledge of linear programming and probability, and some introduction to modeling from operations research, management science or something similar. Some facility with compiling and running programs in C++ is required to run the software examples.