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这项研究解决了多阶段决策的问题，并在长期计划过程中不确定，尤其是那些通常由行业计划在整个产品生命周期中计划的问题。 这项工作旨在开发一些算法，以扩展针对两个阶段问题的随机分解模型的早期工作。 这项研究将两个阶段的问题扩展到涉及两个以上阶段的问题。 随机分解SD使用弯曲器内随机生成的观测值，两级随机线性程序的分解以识别最佳解决方案（渐近）。 旨在提高公司全球竞争力的许多关键决策涉及几个方面计划和设计的整合。 作为这项研究的一部分，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，同时降低成本。 在这方面，这项研究的发展可以在同时工程，全面质量管理以及生产计划和控制等领域找到应用。 此外，研究的结果可以提供一些见识，以帮助弥合随机和确定性操作研究之间的差距。