Introduction to Stochastic Programming

John R. Birge, is a Jerry W. and Carol Lee Levin Professor of Operations Management at the University of Chicago Booth School of Business. François Louveaux is a Professor at the University of Namur(FUNDP) in the Department of Business Administration

• Introduction and Examples.- Uncertainty and Modeling Issues.- Basic Properties and Theory.- The Value of Information and the Stochastic Solution.- Two-Stage Recourse Problems.- Multistage Stochastic Programs.- Stochastic Integer Programs.- Evaluating and Approximating Expectations.- Monte Carlo Methods.- Multistage Approximations.- Sample Distribution Functions.- References.

From the reviews of the second edition: "Help the students to understand how to model uncertainty into mathematical optimization problems, what uncertainty brings to the decision process and which techniques help to manage uncertainty in solving the problems. ... certainly attract also the wide spectrum of readers whose main interest lies in possible exploitation of stochastic programming methodology and will help them to find their own way to treat actual problems using stochastic programming methods. As a whole, the three main building blocks of stochastic programming ... are well represented and balanced." (Jitka Dupacova, Zentralblatt MATH, Vol. 1223, 2011)