research
∙
07/08/2023
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks
The load planning problem is a critical challenge in service network des...
share
research
∙
04/23/2023
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch
The paper proposes a novel End-to-End Learning and Repair (E2ELR) archit...
research
∙
01/23/2023
Two-Stage Learning For the Flexible Job Shop Scheduling Problem
The Flexible Job-shop Scheduling Problem (FJSP) is an important combinat...
research
∙
01/21/2023
Compact Optimization Learning for AC Optimal Power Flow
This paper reconsiders end-to-end learning approaches to the Optimal Pow...
research
∙
11/28/2022
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Reliability Assessment Commitment (RAC) Optimization is increasingly imp...
research
∙
09/26/2022
Just-In-Time Learning for Operational Risk Assessment in Power Grids
In a grid with a significant share of renewable generation, operators wi...
research
∙
04/02/2022
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewab...
research
∙
12/27/2021
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
The Security-Constrained Economic Dispatch (SCED) is a fundamental optim...
research
∙
09/25/2020
