Robustness-Optimality Tradeoff for Watershed Load Reduction Decision Making under Deep Uncertainty
Han Su, Feifei Dong, Yong Liu, Rui Zou,Huaicheng Guo
ABSTRACT: Practical and optimal reduction of watershed loads under deep uncertainty, like other traditional optimizations, requires sufficient search alternatives and direct evaluation of robustness. These two requirements are crucial for making robust decisions and they are not well addressed in previous studies. This study thereby (a) uses preconditioning technique in Evolutionary Algorithm to reduce unnecessary search space, which enables a sufficient search, and (b) derives Robustness Index (RI) as a second-tier optimization objective function to achieve refined solutions (solved by GA) that address both robustness and optimality. Uncertainty-based Refined Risk Explicit Linear Interval Programming (RREILP) is used to generate initial solutions (solved by Controlled elitist NSGA-II). The RI calculation error is also quantified. The proposed approach is applied to Lake Dianchi, China. Results demonstrate obvious improvement in robustness after conducting sufficient search and negative robustness-optimality trade-offs, and provides a detailed characteristic of robustness that can serve as references for decision-making.
KEYWORDS: Robustness Index; Deep Uncertainty; Tradeoff; Optimality; Load Reduction