Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling

Abstract

Gasoline blending scheduling is challenging, involving multiple conflicting objectives and a large decision space with many mixed integers. Due to these difficulties, one promising solution is to use preference-based multiobjective evolutionary algorithms (PBMOEAs). However, in practical applications, suitable preferences of decision makers are often difficult to generalize and summarize from their operational experience. This article proposes a novel framework called preference prediction-based evolutionary multiobjective optimization (PP-EMO). In PP-EMO, suitable preferences for a new environment can be automatically obtained from historical operational experience by a machine learning-based preference prediction model when we feed the model with the input of the optimization environment. We have found that the predicted preference is able to guide the optimization to efficiently obtain a set of promising scheduling scenarios. Finally, we conducted comparative tests across various environments, and the experimental results demonstrate that the proposed PP-EMO framework outperforms existing methods. Compared with no preference, PP-EMO reduces operating costs by about 25% and decreases blending errors by 50% under demanding operational conditions.