期刊
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
卷 51, 期 19, 页码 6769-6788出版社
AMER CHEMICAL SOC
DOI: 10.1021/ie201651s
关键词
-
资金
- National Science Foundation [CMMI-0856021]
- National Institute of Health [5R01LM009338]
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [0941143] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [0856021] Funding Source: National Science Foundation
Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, interval+ellipsoidal, and interval+polyhedral uncertainty sets (Li, Z.; Ding, R.; Floudas, C.A. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization. Ind. Eng. Chem. Res. 2011, SO, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据