文献类型：期刊文献

英文题名：Machine learning-based data-driven robust optimization approach under uncertainty

作者：Zhang, Chenhan[1];Wang, Zhenlei[1];Wang, Xin[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200240, Peoples R China

年份：2022

卷号：115

起止页码：1

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20222012111724);WOS:【SCI-EXPANDED(收录号:WOS:000809739600001)】；

基金：This work is supported by National Key R&D Program of China (2018YFB1701103) , National Natural Science Fund for Dis-tinguished Young Scholars (61925305) , National Natural Science Foundation of China (62073142, 62173145) and Shanghai AI Lab.

语种：英文

外文关键词：Machine learning; Data-driven robust optimization; Partial least squares; Kernel principal component analysis; Scheduling problem

摘要：On the basis of the machine learning ability to analyze massive data, we propose a new concept suitable for data-driven robust optimization, and design two new methods for constructing data-driven uncertainty sets. Partial least squares (PLS) or kernel principal component analysis (KPCA) is selected to capture the underlying uncertainties and correlation of uncertain data, and the projection of uncertain data on each principal component is obtained. Then, the probability distribution information of project data is extracted via robust kernel density estimation (RKDE). Considering the applicability of PLS to linear data for the idea of canonical correlation analysis and the bias of KPCA to nonlinear data due to kernel function, guidelines for selecting an appropriate method are presented in terms of the linear and nonlinear degree between data. The measurement indicators are Pearson correlation coefficient, mutual information and nonlinear coefficient. The induced robust counterpart framework not only alleviates excessive conservatism, but also significantly improves the robustness, which has the advantages of easy implementation and high computational efficiency. Through a numerical example and two application cases, the effectiveness of the proposed framework is illustrated.(c) 2022 Elsevier Ltd. All rights reserved.