9523275 Birge This research is a continuation of a previous NSF funded project on stochastic programming. Specifically, this project will continue the development of solution methods, approximation procedures, models , and structural results for multiperiod stochastic programs. These objectives will be accomplished through the development of new decomposition methods for distributed processors, including integer variables and nonlinear functions, new methods with interior point techniques that focus on distributed solutions, new bounds on the value of the stochastic solution and approximation solutions, new techniques for bounding stochastic integer programming problems, and new approximations and methods using specific model characteristics to enable accurate results. Cmmparisons will consider alternative approaches with computational effort, error analysis, and the value of information and model complexity. The models will be applied to a variety of problems drawn from manufacturing, finance, vehicle routing, power systems planning, and energy policy. The goals are to obtain efficient practical solutions with known error characteristics. Many optimization problems are characterized by parameter values that are not known with certainty. Stochastic programming recognizes the uncertain nature of parameters to model and solve problems. The algorithms developed in this research will provide additional capabilities for solving complex decision problems which generally possess greater number of parameters with uncertain values. The explicit recognition of uncertainty in model building combined with more efficient algorithms for solving the models will lead to higher decision quality. The impact of improved decision quality from an economic point of view can be very significant. Further, the models investigated and the solution techniques produced will help to advance the knowledge in the area.
