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Found 13 papers, 1 papers with code
Compact Optimality Verification for Optimization Proxies
Recent years have witnessed increasing interest in optimization proxies, i. e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions.
Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling
AGGNNI-CG also produces significant improvements in service compared to the existing system.
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks
Moreover, this paper designs an optimization proxy that addresses the computational challenges of the optimization model.
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch
The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems.
Two-Stage Learning For the Flexible Job Shop Scheduling Problem
The Flexible Job-shop Scheduling Problem (FJSP) is an important combinatorial optimization problem that arises in manufacturing and service settings.
Compact Optimization Learning for AC Optimal Power Flow
This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA).
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
However, use of FT is problematic due to Gibbs phenomenon.
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors.
Just-In-Time Learning for Operational Risk Assessment in Power Grids
In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i. e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions.
Improving Learning to Branch via Reinforcement Learning
Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP).
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information.
