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)
