141 citations · 237 across the 46 of their papers we have counts for
19 papers · 1 filter
Distributed Linear Programming on GPU Clusters at Extreme Scale
Arnaud Deza, Santanu Dey, Pascal Van Hentenryck
Large linear programs can exceed the memory of a single compute node. Although first-order methods replace sparse factorizations with GPU-suited matrix-vector products, other solve…
End-to-End Supply Chain Planning in the Paper Industry Via Column Generation and Benders Decomposition
Changkun Guan, Amira Hijazi, Pascal Van Hentenryck
Problem definition: The paper studies an integrated end-to-end planning problem in large-scale paper manufacturing, where production scheduling, trimming decisions, vehicle loading…
The Proxy Benders Decomposition
Changkun Guan, El Mehdi Er Raqabi, Mathieu Tanneau +1
Benders decomposition is a fundamental framework for solving large-scale mixed-integer optimization problems with complicating variables that, when fixed, yield significantly easie…
A Hybrid Decomposition Approach for Stochastic Unit Commitment with Combined-Cycle Generators
Rosemary Barrass, Harsha Nagarajan, Mathieu Tanneau +2
The U.S. power grid is undergoing a paradigm shift as energy demand grows in scale and volatility. In response to this growing need, the U.S. has increased adoption of combined-cyc…
Paratransit Optimization with Constraint Programming: A Case Study in Savannah, Georgia
Liam Jagrowski, Kevin Dalmeijer, Tinghan Ye +1
Paratransit services are vital for individuals who cannot use fixed-route public transit, including those with disabilities. Optimizing these services is essential for transit agen…
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…
Dual Lagrangian Learning for Conic Optimization
Mathieu Tanneau, Pascal Van Hentenryck
This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies. DLL leverages conic duality and the representation power…
Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
Haoruo Zhao, Hassan Hijazi, Haydn Jones +3
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their d…
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…
Revitalizing Public Transit in Low Ridership Areas: An Exploration of On-Demand Multimodal Transit Systems
Jiawei Lu, Connor Riley, Krishna Murthy Gurumurthy +1
Public transit plays an essential role in mitigating traffic congestion, reducing emissions, and enhancing travel accessibility and equity. One of the critical challenges in design…
On the Viability of Stochastic Economic Dispatch for Real-Time Energy Market Clearing
Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck
Over the past decade, the rapid adoption of intermittent renewable energy sources (RES), especially wind and solar generation, has posed challenges in managing real-time uncertaint…
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…
Path-Based Formulations for the Design of On-demand Multimodal Transit Systems with Adoption Awareness
Hongzhao Guan, Beste Basciftci, Pascal Van Hentenryck
This paper reconsiders the ODMTS Design with Adoptions problem (ODMTS-DA) to capture the latent demand in on-demand multimodal transit systems. The ODMTS-DA is a bilevel optimizati…
Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems
Hongzhao Guan, Beste Basciftci, Pascal Van Hentenryck
Capturing latent demand has a pivotal role in designing transit services as omitting these riders can lead to poor quality of service and/or additional costs. This paper explores t…
Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
Convex optimization finds many real-life applications, where--optimized on real data--optimization results may expose private data attributes (e.g., individual health records, comm…
A Linear Outer Approximation of Line Losses for DC-based Optimal Power Flow Problems
Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck
This paper proposes a novel and simple linear model to capture line losses for use in linearized DC models, such as optimal power flow (DC-OPF) and security-constrained economic di…
Ridesharing and Fleet Sizing For On-Demand Multimodal Transit Systems
Ramon Auad, Pascal Van Hentenryck
This paper considers the design of On-Demand Multimodal Transit Systems (ODMTS) that combine fixed bus/rail routes between transit hubs with on-demand shuttles that serve the first…
Unit Commitment With Gas Network Awareness
Geunyeong Byeon, Pascal Van Hentenryck
Recent changes in the fuel mix for electricity generation and, in particular, the increase in Gas-Fueled Power Plants (GFPP), have created significant interdependencies between the…