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Performance Models with Data Evolution

数据演化的性能模型

基本信息

批准号：
9978780
负责人：
Julia Higle
金额：
$ 41.63万
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：
1999
资助国家：
美国
起止时间：
1999-09-01 至 2003-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9978780&HistoricalAwards=false
关键词：
Performance Models Data Evolution

项目摘要

Integer and combinatorial optimization models have many applications in engineering design as well as operations of manufacturing and distribution systems. For example, issues dealing with setup timereduction, batch size determination, scheduling, routing, etc. often call for integer and combinatorial optimization models. Traditional approaches to these problems assume that data are known with certainty. In many practical problems, data are not only uncertain, but also evolve over time. This research is devotedto models and algorithms that allow data evolution within integer programming models. This project's approach to data evolution draws upon the theory of stochastic programming, which has mainly focused on linear and convex optimization models. Because there are effective algorithms for stochastic linear and convex optimization problems, this research is dedicated to developing convex approximations for stochastic integer programs. The decision making problems to be considered involve a decision process and a data evolution process that are interwoven over time. In these models, resource commitments and operations are modeled using a mixed integer program, while data evolution is captured via a scenario tree. By combining mixed integer programming with stochastic optimization, this project will provide the next generation of Operations Research models for decision making under uncertainty.

整数和组合优化模型在工程设计以及制造和分销系统的操作中具有许多应用。例如，处理设置时间减少、批量大小确定、调度、路由等的问题通常需要整数和组合优化模型。解决这些问题的传统方法假设数据是确定的。在许多实际问题中，数据不仅是不确定的，而且会随着时间的推移而演变。这项研究致力于允许整数规划模型内的数据演化的模型和算法。该项目的数据演化方法借鉴了随机规划理论，该理论主要关注线性和凸优化模型。由于存在针对随机线性和凸优化问题的有效算法，因此本研究致力于开发随机整数规划的凸近似。要考虑的决策问题涉及随着时间的推移交织在一起的决策过程和数据演化过程。在这些模型中，资源承诺和操作是使用混合整数程序建模的，而数据演变是通过场景树捕获的。通过将混合整数规划与随机优化相结合，该项目将为不确定性下的决策提供下一代运筹学模型。