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

该学术联络与工业研究项目（GOALI）的主要目标是开发理论、算法和计算技术，以解决化学过程中不确定的短期调度问题。以下具体目标将进行研究。在目标1中，将研究一种新的鲁棒优化框架，用于解决不确定性问题。在目标2中，将研究目标1中的鲁棒优化框架在间歇和连续化工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、中间交货日期和大量不确定参数的需求。这项工作将产生新的鲁棒优化模型，为解决化学系统短期调度中的不确定性奠定基础。一个独特的贡献是这个框架可以处理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各种学科中都有应用，因此这是所提出工作的通用组成部分。这些理论和算法的发展将与工业案例研究相辅相成，并将受益于我们与阿托菲纳化学公司研究小组的合作。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鲁棒优化框架。

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.