GOALI: Short-term Scheduling Under Uncertainty: A Robust Optimization Framework

GOALI：不确定性下的短期调度：鲁棒优化框架

基本信息

批准号：
0355336
负责人：
Christodoulos Floudas
金额：
$ 9万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2004
资助国家：
美国
起止时间：
2004-08-15 至 2008-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0355336&HistoricalAwards=false
关键词：
GOALI Short term Scheduling Under

项目摘要

The primary objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) research project is to develop theoretical, algorithmic and computational techniques for addressing the short-term scheduling under uncertainty in chemical processes. The following specific aims will be investigated. In Aim 1, a novel robust optimization framework for addressing uncertainty issues will be studied. In Aim2, the application of the robust optimization framework in Aim 1 to the short-term scheduling of batch and continuous chemical processes which feature multiproduct, multipurpose plants with demands at intermediate due dates and a large number of uncertain parameters will be investigated.The work will result in novel robust optimization models that will form the foundation for addressing the uncertainly in the short-term scheduling of chemical systems. A unique contribution is that this framework can address uncertainly issues for models that contain a large number of uncertain parameters. These robust optimization formulations for mixed-integer linear and nonlinear models have applications in a variety of disciplines, and hence this is a generic component in the proposed work. These theoretical and algorithmic developments will be complemented with industrial case studies and will benefit from our collaboration with the research group at ATOFINA Chemicals Inc. These industrial case studies will be used to validate the robust optimization framework for short-term scheduling chemical systems under uncertainty.

该资助学术与工业联络机会 (GOALI) 研究项目的主要目标是开发理论、算法和计算技术，以解决化学过程不确定性下的短期调度问题。将研究以下具体目标。在目标 1 中，将研究一种用于解决不确定性问题的新型鲁棒优化框架。在目标 2 中，将研究目标 1 中的鲁棒优化框架在批量和连续化学过程的短期调度中的应用，这些过程具有多产品、多用途工厂、中间到期日需求和大量不确定参数的特点。将产生新颖的鲁棒优化模型，为解决化学系统短期调度的不确定性奠定基础。该框架的独特贡献是可以解决包含大量不确定参数的模型的不确定性问题。这些用于混合整数线性和非线性模型的鲁棒优化公式在多种学科中都有应用，因此这是拟议工作中的通用组成部分。这些理论和算法的发展将得到工业案例研究的补充，并将受益于我们与 ATOFINA Chemicals Inc. 研究小组的合作。这些工业案例研究将用于验证不确定性下短期调度化学系统的稳健优化框架。