.png){kind=icon}
Return
Robust Optimization for the Pooling Problem
.png){kind=icon}
OA
AI
DOI：10.1021/acs.iecr.9b01772.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The pooling problem has applications, for example, in petrochemical refining, water networks, and supply chains and is widely studied in global optimization. To date, it has largely been treated deterministically, neglecting the influence of parametric uncertainty. This paper applies two robust optimization approaches, reformulation and cutting planes, to the nonlinear, nonconvex pooling problem. Most applications of robust optimization have been either convex or mixed-integer linear problems. We explore the suitability of robust optimization in the context of global optimization problems which are concave in the uncertain parameters by considering the pooling problem with uncertain inlet concentrations. We compare the computational efficiency of reformulation and cutting plane approaches for three commonly used uncertainty set geometries on 14 pooling problem instances and demonstrate how accounting for uncertainty changes the optimal solution.
Keywords:
GLOBAL OPTIMIZATION
GENERALIZED SEMIINFINITE
APPROXIMATION
FORMULATIONS
CONSTRAINTS
ALGORITHM
PROGRAMS
DUALITY
SAFETY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W
Organization
Cited Papers
Prevalence of polyps in an autopsy series from areas with varying incidence of large‐bowel cancer
.png){kind=icon}
PREAI
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more