The aim of stochastic programming is to find optimal decisions
in problems which involve uncertain data. This field is currently
developing rapidly with contributions from many disciplines
including operations research, mathematics, and probability. At the
same time, it is now being applied in a wide variety of subjects
ranging from agriculture to financial planning and from industrial
engineering to computer networks. This textbook provides a first
course in stochastic programming suitable for students with a basic
knowledge of linear programming, elementary analysis, and
probability. The authors aim to present a broad overview of the
main themes and methods of the subject. Its prime goal is to help
students develop an intuition on how to model uncertainty into
mathematical problems, what uncertainty changes bring to the
decision process, and what techniques help to manage uncertainty in
solving the problems.
In this extensively updated new edition there is more material on
methods and examples including several new approaches for discrete
variables, new results on risk measures in modeling and Monte Carlo
sampling methods, a new chapter on relationships to other methods
including approximate dynamic programming, robust optimization and
online methods.
The book is highly illustrated with chapter summaries and many
examples and exercises. Students, researchers and practitioners in
operations research and the optimization area will find it
particularly of interest.
Review of First Edition:
"The discussion on modeling issues, the large number of examples
used to illustrate the material, and the breadth of the coverage
make'Introduction to Stochastic Programming' an ideal textbook for
the area." (Interfaces, 1998)
"
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