Journal
CHINESE JOURNAL OF CHEMICAL ENGINEERING
Volume 26, Issue 8, Pages 1700-1706Publisher
CHEMICAL INDUSTRY PRESS CO LTD
DOI: 10.1016/j.cjche.2017.09.010
Keywords
Optimization under uncertainty; Robust optimization; Stochastic programming; Chance constrained programming; Data-driven optimization
Categories
Ask authors/readers for more resources
Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering. Since the solution of an optimization problemgenerally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data- driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data. (C) 2017 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available