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Found 50 papers, 4 papers with code
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e. g., wind/solar), dispatchable devices (e. g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization.
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
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.
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
This paper surveys recent work in the intersection of differential privacy (DP) and fairness.
Post-processing of Differentially Private Data: A Fairness Perspective
Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees.
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.
End-to-end Learning for Fair Ranking Systems
The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.
Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions
The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization.
Data-Driven Time Series Reconstruction for Modern Power Systems Research
A critical aspect of power systems research is the availability of suitable data, access to which is limited by privacy concerns and the sensitive nature of energy infrastructure.
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method
The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes.
Public Transit for Special Events: Ridership Prediction and Train Optimization
This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events.
Learning Hard Optimization Problems: A Data Generation Perspective
Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy.
Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization
The MPC optimization operates over a longer time horizon to compensate for the inherent myopic nature of the real-time dispatching.
Decision Making with Differential Privacy under a Fairness Lens
Agencies, such as the U. S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes.
End-to-End Constrained Optimization Learning: A Survey
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.
Ridesharing and Fleet Sizing For On-Demand Multimodal Transit Systems
The results demonstrate the substantial potential of ridesharing for ODMTS, as costs are reduced by about 26% with respect to allowing only individual shuttle rides, at the expense of a minimal increase in transit times.
Optimization and Control
Spatial Network Decomposition for Fast and Scalable AC-OPF Learning
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training.
Capturing Travel Mode Adoption in Designing On-demand Multimodal Transit Systems
Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude.
Bilevel Optimization
Optimization and Control
Commuting with Autonomous Vehicles: A Branch and Cut Algorithm with Redundant Modeling
To remedy this limitation, the mini-route MIP is complemented by a DARP formulation which is not as effective in obtaining primal solutions but has a stronger relaxation.
Autonomous Vehicles
Optimization and Control
Bias and Variance of Post-processing in Differential Privacy
Moreover, when the feasible region is convex, a widely adopted class of post-processing steps is also guaranteed to improve accuracy.
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age.
The Benefits of Autonomous Vehicles for Community-Based Trip Sharing
Prior work motivated by reducing parking pressure and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling platform for community-based trip sharing could reduce the number of vehicles by close to 60%.
Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow
The security-constrained optimal power flow (SCOPF) is fundamental in power systems and connects the automatic primary response (APR) of synchronized generators with the short-term schedule.
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
It is non-convex and NP-hard, and computationally challenging for large-scale power systems.
Differential Privacy of Hierarchical Census Data: An Optimization Approach
To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
Differentially Private Convex Optimization with Feasibility Guarantees
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.
Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling
We present a novel framework for modeling traffic congestion events over road networks.
Electricity-Aware Bid Format for Heat Commitment and Dispatch
Coordination between heat and electricity markets is essential to achieve a cost-effective and efficient operation of the overall energy system.
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-sharing systems to address congestion issues faced by many cities.
Bilevel Optimization for Differentially Private Optimization in Energy Systems
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive.
Lagrangian Duality for Constrained Deep Learning
In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Furthermore, the paper provides a comprehensive comparison of student models with the benchmark model (decision tree) and the teacher model (gradient boosting trees) to quantify the fidelity and accuracy of the students' interpretations.
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems.
Privacy-Preserving Obfuscation of Critical Infrastructure Networks
The paper studies how to release data about a critical infrastructure network (e. g., the power network or a transportation network) without disclosing sensitive information that can be exploited by malevolent agents, while preserving the realism of the network.
The Commute Trip Sharing Problem
In particular, the paper formalizes the Commute Trip Sharing Problem (CTSP) to find a routing plan that maximizes ride sharing for a set of commute trips.
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
The results on the case study show that the machine-learning classifier, together with model-agnostic interpretation tools, provides valuable insights on travel mode switching behavior for different individuals and population segments.
Differential Privacy for Power Grid Obfuscation
To address these concerns, this paper presents a novel differential privacy mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated network.
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved in the field of travel behavior modeling.
OptStream: Releasing Time Series Privately
Then, the perturbation module adds noise to the sampled data points to guarantee privacy.
Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations
This paper considers the integrated problem of quay crane assignment, quay crane scheduling, yard location assignment, and vehicle dispatching operations at a container terminal.
Graphical Models for Optimal Power Flow
In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors.
Interdependent Scheduling Games
We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently.
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption.
Social and Information Networks
Benders Decomposition for the Design of a Hub and Shuttle Public Transit System
The BusPlus project aims at improving the off-peak hours public transit service in Canberra, Australia.
A Constraint Programming Approach for Non-Preemptive Evacuation Scheduling
Large-scale controlled evacuations require emergency services to select evacuation routes, decide departure times, and mobilize resources to issue orders, all under strict time constraints.
The QC Relaxation: Theoretical and Computational Results on Optimal Power Flow
Convex relaxations of the power flow equations and, in particular, the Semi-Definite Programming (SDP) and Second-Order Cone (SOC) relaxations, have attracted significant interest in recent years.
Computational Engineering, Finance, and Science Optimization and Control
Domain Views for Constraint Programming
Traditional constraint-programming systems provide the concept of {\em variable views} which implement a view of the type $y = f(x)$ by delegating all (domain and constraint) operations on variable $y$ to variable $x$.
A Microkernel Architecture for Constraint Programming
This paper presents a microkernel architecture for constraint programming organized around a number of small number of core functionalities and minimal interfaces.
Propagating Regular Counting Constraints
Constraints over finite sequences of variables are ubiquitous in sequencing and timetabling.
A Linear-Programming Approximation of AC Power Flows
Linear active-power-only DC power flow approximations are pervasive in the planning and control of power systems.
