文献类型：期刊文献

英文题名：Data-driven distributionally robust optimization under combined ambiguity for cracking production scheduling

作者：Zhang, Chenhan[1];Wang, Zhenlei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：181

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20235015197872);WOS:【SCI-EXPANDED(收录号:WOS:001135295700001)】；

基金：This work is supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , Project of Shanghai Gas Turbine Union Innovation Center, National Natural Science Foun-dation of China (62273149, 62373153, 62373154) and Fundamental Research Funds for the Central Universities (222202417006) .

语种：英文

外文关键词：Data-driven decision making; Distributionally robust optimization; Moment information; Wasserstein distance; Cyclic scheduling

摘要：Distributionally robust optimization has garnered significant attention for its effectiveness in decision-making under uncertainty. However, employing this strategy faces hurdles posed by intractable models and the difficulty in parameter determination while tackling production scheduling issues under uncertainty. This work presents a novel data-driven distributionally robust optimization framework to address these challenges. data-driven combined ambiguity set, which incorporates Wasserstein distance and moment information, devised to yield less conservative solutions. Additionally, a data-driven support set established based on improved kernel technique is introduced to help identify and exclude potential outliers. The relevant ambiguous parameters are determined through bi-level cross-validation. Subsequently, the data-driven distributionally robust optimization model under combined ambiguity is reformulated into tractable by dual theory. The application to industrial scheduling shows that the proposed method can effectively utilize data information and better hedge against uncertainties while obtaining higher profits.