Computational and Applied Aspects of Multiperiod Stochastic Programming

这个项目将开发多周期随机规划的近似和求解程序的方法。该项目将继续开发和使用来自不同领域的现实模型，包括制造、金融、车辆路径和能源政策。其主要目标是有效地获得具有已知误差特征的实用解。这些目标将通过开发用于分布式处理机的新的分解和内点方法、随机解的值的新界限、约束随机非线性规划问题的新技术以及使用参数解和连续分布近似的新近似来实现。随着随机规划模型库的不断开发，将对这些方法进行评估。比较将考虑具有计算工作量、误差分析以及信息价值和模型复杂性的替代方法。

This project will develop methods for approximations and solution procedures for multiperiod stochastic programs. The project will continue to develop and use realistic models from a variety of areas, including manufacturing, finance, vehicle routing, and energy policy. The major goals are to obtain practical solutions with known error characteristics efficiently. These goals will be accomplished through the development of new decomposition and interior point methods for distributed processors, new bounds on the value of the stochastic solution, new techniques for bounding stochastic nonlinear programming problems, and new approximations using parametric solutions and continuous distribution approximations. These methods will be assessed with the ongoing development of the stochastic programming model library. Comparisons will consider alternative approaches with computational effort, error analyses, and values of information and model complexity.