578 Data Driven Optimization

Wednesday, November 18, 2020: 8:00 AM - 9:00 AM
Computing and Systems Technology Division (10) (PreRecorded+)

Description:
Data from simulations or from process industries can be high-dimensional, sparse, uncertain, heterogeneous, multi-scale and represent discontinuous nonlinear functions. Novel methodology is required for data-driven optimization for applications in design, real-time optimization, scheduling, and process operations. This session seeks presentations on new mathematical optimization algorithms for data-driven optimization and/or applications to the process industries.

Sponsor:
Information Management and Intelligent Systems
Co-Sponsor(s):
Systems and Process Operations (10C)
Chair:
Fani Boukouvala Email: fani.boukouvala@chbe.gatech.edu
Co-Chair:
M. M. Faruque Hasan Email: hasan@tamu.edu


(578a) The Melon Toolbox: Machine Learning Models for Optimization
Artur M. Schweidtmann, Linus Netze and Alexander Mitsos


(578b) Surrogate-Based Optimization Using Random Forests
Zuo Zeng, Bianca Williams and Selen Cremaschi


(578c) Data-Driven Process Optimization Under Neural Network Surrogate Model Uncertainty
Shu-Bo Yang and Zukui Li


(578d) A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty and Active Learning
Qi Zhang and Wei Feng


(578e) Simulation-Based Optimization with Embedded Dynamic Models Using a Data-Driven Spatial Branch-and-Bound Algorithm
Jianyuan Zhai and Fani Boukouvala


(578f) Data-Driven Strategies for Optimization of Integrated Chemical Plants
Kaiwen Ma, Nick Sahinidis, Sreekanth Rajagopalan, Satyajith Amaran and Scott J. Bury


(578h) On the Acceleration of quadratic Semidefinite Outer-Approximation Using Combinations of Cutting Plane Approximations and Data Analytics
Antonis C. Kokossis and Asimina Marousi
See more of this Group/Topical: Computing and Systems Technology Division
<< Previous Session | Next Session >>