.png){kind=icon}
返回
A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization
.png){kind=icon}
OA
AI
DOI：10.1021/ie200150p.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left-hand side, right-hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented.
Keyword:
TRACTABLE APPROXIMATIONS
UNCERTAIN DATA
PROGRAMS
NETWORK
DESIGN
ALGORITHM
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
机构
引用论文
Effects of Group Counseling Transmitted Through Videoconferencing on Changes in Eating Behaviors
PREAI
分享收藏Understanding the Effect of Side Reactions on the Recyclability of Furan–Maleimide Resins Based on Thermoreversible Diels–Alder Network.png)
Polymers
OAAI
PolymersIF0
OAAI分享收藏
查看更多内容