Integrated Planning Under Uncertainty: Statistical Methods in Mathematical Programming

不确定性下的综合规划：数学规划中的统计方法

• 批准号: 9414680
• 负责人: Suvrajeet Sen
• 金额: $ 30.12万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-11-01 至 1998-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9414680&HistoricalAwards=false
• 关键词: Integrated; Planning; Under; Uncertainty; Statistical

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.

9414680 Sen本研究解决了在长期规划过程中具有不确定性的多阶段决策问题，特别是那些通常由工业在规划产品的整个生命周期时所承担的问题。这项工作的目的是开发一些算法来扩展早期在两阶段问题的随机分解模型中的工作。本研究将两阶段问题扩展为涉及两个以上阶段的问题。随机分解SD使用在Benders内随机生成的观测值，分解一个两阶段的随机线性程序来确定最优解（渐近）。许多旨在提高公司全球竞争力的关键决策涉及规划和设计几个方面的整合。作为本研究的一部分开发的技术将提供必要的工具，以整合公司在设计高质量和持久产品时的几个决策要素，同时保持低成本。在这方面，本研究的发展可以在并行工程、全面质量管理和生产计划与控制等领域找到应用。此外，研究结果可以提供一些见解，以帮助弥合随机和确定性运筹学之间的差距。