Mathematical Programming Under Uncertainty: Risk and Recourse Revisited

不确定性下的数学规划：重新审视风险和追索权

基本信息

批准号：
9114352
负责人：
Suvrajeet Sen
金额：
$ 24.66万
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：
1991
资助国家：
美国
起止时间：
1991-09-01 至 1995-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9114352&HistoricalAwards=false
关键词：
Mathematical Programming Under Uncertainty Risk

项目摘要

With increased competition in the marketplace, many sectors of the American economy are acknowledging the need for improved resource planning. By way of example, note the growing acceptance of the Just-In-Time philosophy in many manufacturing firms, the move toward dynamic routing capabilities within the telecommunication industry and adaptive signal control in urban traffic planning. In each of these areas, resources (e.g., productive capabilities and network capacities) are acquired well in advance of their need, and utilization is improved by adaptive allocation as precise system requirements are identified. Since precise information regarding system operation is not available at the time of planning, the resource planning problem involves decision making under uncertainty. This project focusses on modeling and solution of planning problems for systems that allow adaptive resource allocation. These methodologies are based on stochastic decomposition. Computational experience with stochastic decomposition indicates that it is ideally suited to the solution of large scale problems involving optimal planning under uncertainty. It is particularly useful when the variability of system requirements can only be assessed via simulated scenarios. The project involves three phases, involving the mathematical development and verification of these methods, computer implementation of the resulting algorithms, and testing of these algorithms on models based on case-studies which will be developed in conjunction with industrial collaborators.

随着市场竞争的加剧，美国经济的许多部门都认识到需要改进资源规划。举例来说，请注意许多制造企业越来越多地接受“准时制”理念、电信行业内向动态路由功能的转变以及城市交通规划中的自适应信号控制。在每个领域，资源（例如，生产能力和网络容量）都是在其需求之前获得的，并且在确定精确的系统需求时通过自适应分配来提高利用率。由于在规划时无法获得有关系统运行的精确信息，因此资源规划问题涉及不确定性下的决策。该项目重点关注允许自适应资源分配的系统的规划问题的建模和解决方案。这些方法基于随机分解。随机分解的计算经验表明，它非常适合解决涉及不确定性下最优规划的大规模问题。当系统需求的变化只能通过模拟场景进行评估时，它特别有用。该项目分为三个阶段，包括这些方法的数学开发和验证、所得算法的计算机实现以及基于案例研究的模型对这些算法的测试，这些案例研究将与工业合作者一起开发。