ROBUSTNESS METRICS FOR DYNAMIC OPTIMIZATION MODELS UNDER PARAMETER UNCERTAINTY

Recent research in process systems engineering has focused mostly on t he issue of making decisions under uncertainty. Various approaches use d over the years include optimizing the expected and worst cases, maxi mizing the feasibility of operation, and constraining variances of per formance measures. The consideration of robustness, that is, guarantee ing a reasonable performance over a wide range of uncertainty, is eith er implicit or explicit in these approaches, and is certainly receivin g more attention. In this article, we argue that mathematical techniqu es for robust optimization must be capable of capturing different pers pectives on risk of different users. We define some general robustness metrics that can represent significantly different robustness objecti ves simply by modifying functions and parameters. We also describe a s olution procedure along with two illustrative examples.