The area of process systems engineering (PSE) is primarily concerned with the design and operation of chemical processes. With regard to optimization under uncertainty, the PSE community has worked on what is referred to as flexibility analysis since the 1970s, which closely resembles what is now widely known as robust optimization. However, surprisingly, flexibility analysis has not received any attention in the operations research community. In the first part of this talk, we establish the relationship between flexibility analysis and two-stage adjustable robust optimization (ARO). We further apply ARO methods to develop more efficient solution approaches to flexibility analysis problems for linear systems. In the second part, we present an application of multistage ARO using affine decision rules. Here, we consider the scheduling of power-intensive chemical processes that can provide interruptible load, which refers to backup load reduction capacity that can be sold to provide additional revenue. Uncertainty arises in this problem as one does not know in advance when and how much load reduction will be requested.

Bio: Qi Zhang is an Assistant Professor in the Department of Chemical Engineering and Materials Science at the University of Minnesota. He received his Ph.D. in Chemical Engineering from Carnegie Mellon University, and worked at BASF in Germany and Houston prior to joining the University of Minnesota. His research lies at the intersection of chemical engineering and operations research, primarily focused on developing optimization models and methods for the design of energy and process systems, production planning and scheduling, supply chain optimization, and optimization under uncertainty.