Strategic, operational, and real-time control decisions are often made without complete information about the effects of these decisions. Stochastic programming models incorporate this uncertainty explicitly into the decision-making process. These models quantify uncertainty into distributional information on key model parameters and address an objective that reflects the goals and risk sensitivity of the decision maker. Advances in the theory supporting the models and in computer implementations of this theory have brought stochastic programming to the threshold of widespread, practical use. The International Conference on Stochastic Programming will further this development by bringing together the leading researchers in the field to share their individual findings, to gain insights from the exchange of ideas, and to focus on selected applications that can benefit from stochastic programming. This conference will continue the field's strong tradition of truly international cooperation by including scientists from around the world.
