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08/04/2023
MARTA Reach: Piloting an On-Demand Multimodal Transit System in Atlanta
This paper reports on the results of the six-month pilot MARTA Reach, wh...
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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...
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07/05/2023
AI4OPT: AI Institute for Advances in Optimization
This article is a short introduction to AI4OPT, the NSF AI Institute for...
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06/20/2023
Modern Constraint Programming Education: Lessons for the Future
This paper details an outlook on modern constraint programming (CP) educ...
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05/12/2023
Impacts of Differential Privacy on Fostering more Racially and Ethnically Diverse Elementary Schools
In the face of increasingly severe privacy threats in the era of data an...
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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...
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04/10/2023
Artificial Intelligence/Operations Research Workshop 2 Report Out
This workshop Report Out focuses on the foundational elements of trustwo...
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03/14/2023
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools
Most US school districts draw "attendance boundaries" to define catchmen...
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02/27/2023
Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area
This paper analyzes the impact of COVID-19 related lockdowns in the Atla...
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01/23/2023
Two-Stage Learning For the Flexible Job Shop Scheduling Problem
The Flexible Job-shop Scheduling Problem (FJSP) is an important combinat...
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01/21/2023
Compact Optimization Learning for AC Optimal Power Flow
This paper reconsiders end-to-end learning approaches to the Optimal Pow...
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11/28/2022
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Reliability Assessment Commitment (RAC) Optimization is increasingly imp...
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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...
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08/18/2022
Self-Supervised Primal-Dual Learning for Constrained Optimization
This paper studies how to train machine-learning models that directly ap...
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08/16/2022
Active Bucketized Learning for ACOPF Optimization Proxies
This paper considers optimization proxies for Optimal Power Flow (OPF), ...
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05/08/2022
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use o...
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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...
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12/27/2021
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
The Security-Constrained Economic Dispatch (SCED) is a fundamental optim...
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11/05/2021
Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions
Large-scale ride-hailing systems often combine real-time routing at the ...
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10/26/2021
Data-Driven Time Series Reconstruction for Modern Power Systems Research
A critical aspect of power systems research is the availability of suita...
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08/30/2021
Multi-Resolution Spatio-Temporal Prediction with Application to Wind Power Generation
This paper proposes a spatio-temporal model for wind speed prediction wh...
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06/09/2021
Public Transit for Special Events: Ridership Prediction and Train Optimization
Many special events, including sport games and concerts, often cause sur...
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05/27/2021
Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization
Large-scale ride-sharing systems combine real-time dispatching and routi...
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01/17/2021
Spatial Network Decomposition for Fast and Scalable AC-OPF Learning
This paper proposes a novel machine-learning approach for predicting AC-...
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10/09/2020
Bias and Variance of Post-processing in Differential Privacy
Post-processing immunity is a fundamental property of differential priva...
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09/26/2020
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models who...
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08/28/2020
The Benefits of Autonomous Vehicles for Community-Based Trip Sharing
This work reconsiders the concept of community-based trip sharing propos...
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07/14/2020
Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow
The security-constrained optimal power flow (SCOPF) is fundamental in po...
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06/29/2020
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
The AC Optimal Power Flow (AC-OPF) is a key building block in many power...
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06/28/2020
Differential Privacy of Hierarchical Census Data: An Optimization Approach
This paper is motivated by applications of a Census Bureau interested in...
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06/22/2020
Differentially Private Convex Optimization with Feasibility Guarantees
This paper develops a novel differentially private framework to solve co...
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05/15/2020
Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling
We present a novel framework for modeling traffic congestion events over...
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05/07/2020
Pricing under a multinomial logit model with non linear network effects
We study the problem of pricing under a Multinomial Logit model where we...
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04/08/2020
Differentially Private Optimal Power Flow for Distribution Grids
Although distribution grid customers are obliged to share their consumpt...
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03/24/2020
Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control
This paper considers the dispatching of large-scale real-time ride-shari...
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02/01/2020
Differential Privacy for Stackelberg Games
This paper introduces a differentially private (DP) mechanism to protect...
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01/26/2020
Bilevel Optimization for Differentially Private Optimization
This paper studies how to apply differential privacy to constrained opti...
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01/26/2020
A Lagrangian Dual Framework for Deep Neural Networks with Constraints
A variety of computationally challenging constrained optimization proble...
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11/22/2019
PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets
This paper introduces a differentially private mechanism to protect the ...
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10/30/2019
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Machine learning has proved to be very successful for making predictions...
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10/13/2019
Shared E-scooters: Business, Pleasure, or Transit?
Shared e-scooters have become a familiar sight in many cities around the...
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10/07/2019
Privacy-Preserving Obfuscation for Distributed Power Systems
This paper considers the problem of releasing privacy-preserving load da...
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09/19/2019
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
The Optimal Power Flow (OPF) problem is a fundamental building block for...
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05/23/2019
Privacy-Preserving Obfuscation of Critical Infrastructure Networks
The paper studies how to release data about a critical infrastructure ne...
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04/24/2019
The Commute Trip Sharing Problem
Parking pressure has been steadily increasing in cities as well as in un...
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02/08/2019
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
Recent years have witnessed an increased focus on interpretability and t...
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01/22/2019
Mobility-on-demand versus fixed-route transit systems: an evaluation of traveler preferences in low-income communities
Emerging transportation technologies, such as ride-hailing and autonomou...
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01/21/2019
Differential Privacy for Power Grid Obfuscation
The availability of high-fidelity energy networks brings significant val...
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11/04/2018
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
Logit models are usually applied when studying individual travel behavio...
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08/06/2018