5 citations · 27 across the 12 of their papers we have counts for
12 papers
Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
Lingkai Kong, Anagha Satish, Hezi Jiang +6
Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraint…
Compact Optimality Verification for Optimization Proxies
Wenbo Chen, Haoruo Zhao, Mathieu Tanneau +1
Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems…
Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling
Jiawei Lu, Tinghan Ye, Wenbo Chen +1
Optimizing service schedules is pivotal to the reliable, efficient, and inclusive on-demand mobility. This pressing challenge is further exacerbated by the increasing needs of an a…
Real-Time Risk Analysis with Optimization Proxies
Wenbo Chen, Mathieu Tanneau, Pascal Van Hentenryck
The increasing penetration of renewable generation and distributed energy resources requires new operating practices for power systems, wherein risk is explicitly quantified and ma…
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks
Ritesh Ojha, Wenbo Chen, Hanyu Zhang +3
The load planning problem is a critical challenge in service network design for parcel carriers: it decides how many trailers to assign for dispatch over time between pairs of term…
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch
Wenbo Chen, Mathieu Tanneau, Pascal Van Hentenryck
The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems. E2ELR combines deep neural networks…
Two-Stage Learning For the Flexible Job Shop Scheduling Problem
Wenbo Chen, Reem Khir, Pascal Van Hentenryck
The Flexible Job-shop Scheduling Problem (FJSP) is an important combinatorial optimization problem that arises in manufacturing and service settings. FJSP is composed of two subpro…
Compact Optimization Learning for AC Optimal Power Flow
Seonho Park, Wenbo Chen, Terrence W. K. Mak +1
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability is…
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park, Wenbo Chen, Dahye Han +2
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased…
Just-In-Time Learning for Operational Risk Assessment in Power Grids
Oliver Stover, Pranav Karve, Sankaran Mahadevan +4
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 genera…
Risk-Aware Control and Optimization for High-Renewable Power Grids
Neil Barry, Minas Chatzos, Wenbo Chen +10
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 operatio…
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Wenbo Chen, Seonho Park, Mathieu Tanneau +1
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring relia…