Integrated Planning Under Uncertainty: Statistical Methods in Mathematical Programming

9414680 Sen本研究针对长期规划过程中存在不确定性的多阶段决策问题，特别是在产品整个生命周期规划过程中通常由行业承担的决策问题。这项工作的目的是开发一些算法来扩展早期关于两阶段问题的随机分解模型的工作。本研究将两阶段问题推广到包含两个以上阶段的问题。随机分解SD使用Bders内随机生成的观测值，对两阶段随机线性规划进行分解，以确定最优解(渐近)。许多旨在提高公司全球竞争力的关键决策涉及几个方面的集成规划和设计。作为这项研究的一部分，开发的技术将提供必要的工具，在设计高质量和持久的产品时整合公司的几个决策要素，同时保持低成本。在这方面，本研究的成果可应用于并行工程、全面质量管理、生产计划与控制等领域。此外，研究结果可以为弥合随机运筹学和确定性运筹学之间的差距提供一些见解。

9414680 Sen This research addresses the problem of multi-stage decision making with uncertainty in a long-range planning process, especially those that are typically undertaken by industry in planning for the entire life cycle of a product. The work is aimed at developing some algorithms to extend earlier work in stochastic decomposition models for two stage problems. This research extends the two-stage problem to a problem involving more than two stages. Stochastic decomposition SD uses randomly generated observations within Benders, decomposition of a two stage stochastic linear program to identify an optimal solution (asymptotically). Many key decisions aimed at improving a company's global competitiveness involve the integration of several aspects planning and design. The techniques developed as part of this research will provide the tools necessary to integrate several decision elements of a company in designing high quality and long lasting products while at the same time keeping cost down. In this respect, the developments from this research can find applications in such areas as concurrent engineering, total quality management, and production planning and control. Furthermore, the results from the research could provide some insight to help bridge the gap between stochastic and deterministic Operations Research.