45 citations · 48 across the 8 of their papers we have counts for
9 papers
Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
Stefan Faulkner, Reza Zandehshahvar, Vahid Eghbal Akhlaghi +3
Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logist…
Copula-Based Aggregation and Context-Aware Conformal Prediction for Reliable Renewable Energy Forecasting
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggregated (fleet or system) levels. In…
Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is…
CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing
Sikai Cheng, Reza Zandehshahvar, Haoruo Zhao +4
Channel state information (CSI) prediction is a promising strategy for ensuring reliable and efficient operation of massive multiple-input multiple-output (mMIMO) systems by provid…
PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planning
Vahid Eghbal Akhlaghi, Reza Zandehshahvar, Pascal Van Hentenryck
This paper considers how to fuse Machine Learning (ML) and optimization to solve large-scale Supply Chain Planning (SCP) optimization problems. These problems can be formulated as…
Enhanced Renewable Energy Forecasting and Operations through Probabilistic Forecast Aggregation
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
Accurate and reliable forecasting of renewable energy generation is crucial for the efficient integration of renewable sources into the power grid. In particular, probabilistic for…