Robust multi-objective optimization under multiple uncertainties using the CM-ROPAR approach: case study of water resources allocation in the Huaihe River basin

Abstract

Abstract. Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources are uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinders the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated in a case study of water resources allocation in the Huaihe River basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.