no code implementations • 16 Jun 2012 • Carleton Coffrin, Pascal Van Hentenryck
Linear active-power-only DC power flow approximations are pervasive in the planning and control of power systems.
no code implementations • 27 Sep 2013 • Nicolas Beldiceanu, Pierre Flener, Justin Pearson, Pascal Van Hentenryck
Constraints over finite sequences of variables are ubiquitous in sequencing and timetabling.
no code implementations • 21 Jan 2014 • Laurent Michel, Pascal Van Hentenryck
This paper presents a microkernel architecture for constraint programming organized around a number of small number of core functionalities and minimal interfaces.
no code implementations • 21 Jan 2014 • Pascal Van Hentenryck, Laurent Michel
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 $x$.
1 code implementation • 27 Feb 2015 • Carleton Coffrin, Hassan L. Hijazi, Pascal Van Hentenryck
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
no code implementations • 11 May 2015 • Caroline Even, Andreas Schutt, Pascal Van Hentenryck
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.
no code implementations • 30 Dec 2015 • Arthur Maheo, Philip Kilby, Pascal Van Hentenryck
The BusPlus project aims at improving the off-peak hours public transit service in Canberra, Australia.
1 code implementation • 19 Feb 2016 • Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrian, Honglin Yu, Pascal Van Hentenryck
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
no code implementations • 31 May 2016 • Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh
We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently.
no code implementations • 21 Jun 2016 • Krishnamurthy Dvijotham, Pascal Van Hentenryck, Michael Chertkov, Sidhant Misra, Marc Vuffray
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.
no code implementations • 14 Dec 2017 • Damla Kizilay, Deniz T. Eliiyi, Pascal Van Hentenryck
This paper considers the integrated problem of quay crane assignment, quay crane scheduling, yard location assignment, and vehicle dispatching operations at a container terminal.
no code implementations • 6 Aug 2018 • Ferdinando Fioretto, Pascal Van Hentenryck
Then, the perturbation module adds noise to the sampled data points to guarantee privacy.
no code implementations • 4 Nov 2018 • Xilei Zhao, Xiang Yan, Alan Yu, Pascal Van Hentenryck
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.
1 code implementation • 21 Jan 2019 • Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
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.
no code implementations • 8 Feb 2019 • Xilei Zhao, Xiang Yan, Pascal Van Hentenryck
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.
no code implementations • 24 Apr 2019 • Mohd. Hafiz Hasan, Pascal Van Hentenryck, Antoine Legrain
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.
no code implementations • 23 May 2019 • Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
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.
no code implementations • 19 Sep 2019 • Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems.
no code implementations • 30 Oct 2019 • Xilei Zhao, Zhengze Zhou, Xiang Yan, Pascal Van Hentenryck
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.
no code implementations • 26 Jan 2020 • Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive.
no code implementations • 26 Jan 2020 • Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak, Cuong Tran, Federico Baldo, Michele Lombardi
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.
no code implementations • 24 Mar 2020 • Connor Riley, Pascal Van Hentenryck, Enpeng Yuan
This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities.
no code implementations • 6 May 2020 • Lesia Mitridati, Pascal Van Hentenryck, Jalal Kazempour
Coordination between heat and electricity markets is essential to achieve a cost-effective and efficient operation of the overall energy system.
no code implementations • 15 May 2020 • Shixiang Zhu, Ruyi Ding, Minghe Zhang, Pascal Van Hentenryck, Yao Xie
We present a novel framework for modeling traffic congestion events over road networks.
1 code implementation • 22 Jun 2020 • Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, Pierre Pinson
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.
no code implementations • 28 Jun 2020 • Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu
To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
no code implementations • 29 Jun 2020 • Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
It is non-convex and NP-hard, and computationally challenging for large-scale power systems.
BIG-bench Machine Learning Vocal Bursts Intensity Prediction
no code implementations • 14 Jul 2020 • Alexandre Velloso, Pascal Van Hentenryck
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.
no code implementations • 28 Aug 2020 • Mohd. Hafiz Hasan, Pascal Van Hentenryck
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%.
no code implementations • 26 Sep 2020 • Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck
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.
no code implementations • 9 Oct 2020 • Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto
Moreover, when the feasible region is convex, a widely adopted class of post-processing steps is also guaranteed to improve accuracy.
no code implementations • 4 Jan 2021 • Beste Basciftci, Pascal Van Hentenryck
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
no code implementations • 4 Jan 2021 • Mohd. Hafiz Hasan, Pascal Van Hentenryck
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
no code implementations • 17 Jan 2021 • Minas Chatzos, Terrence W. K. Mak, Pascal Van Hentenryck
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training.
no code implementations • 26 Jan 2021 • Ramon Auad, Pascal Van Hentenryck
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
no code implementations • 30 Mar 2021 • James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.
no code implementations • 16 May 2021 • Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck
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.
no code implementations • 27 May 2021 • Enpeng Yuan, Pascal Van Hentenryck
The MPC optimization operates over a longer time horizon to compensate for the inherent myopic nature of the real-time dispatching.
no code implementations • NeurIPS 2021 • James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck
Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy.
no code implementations • 9 Jun 2021 • Tejas Santanam, Anthony Trasatti, Pascal Van Hentenryck, Hanyu Zhang
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.
no code implementations • 12 Oct 2021 • James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck
The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes.
no code implementations • 26 Oct 2021 • Minas Chatzos, Mathieu Tanneau, Pascal Van Hentenryck
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.
no code implementations • 5 Nov 2021 • Enpeng Yuan, Pascal Van Hentenryck
The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization.
no code implementations • 21 Nov 2021 • James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu
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.
no code implementations • 27 Dec 2021 • Wenbo Chen, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck
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.
no code implementations • 24 Jan 2022 • Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
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.
no code implementations • 16 Feb 2022 • Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu
This paper surveys recent work in the intersection of differential privacy (DP) and fairness.
no code implementations • 2 Apr 2022 • Neil Barry, Minas Chatzos, Wenbo Chen, Dahye Han, Chaofan Huang, Roshan Joseph, Michael Klamkin, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck, Shangkun Wang, Hanyu Zhang, Haoruo Zhao
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.
Uncertainty Quantification Vocal Bursts Intensity Prediction
no code implementations • 11 Apr 2022 • Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
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.
no code implementations • 8 May 2022 • Terrence W. K. Mak, Minas Chatzos, Mathieu Tanneau, Pascal Van Hentenryck
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.
no code implementations • 16 Aug 2022 • Michael Klamkin, Mathieu Tanneau, Terrence W. K. Mak, Pascal Van Hentenryck
This paper considers optimization proxies for Optimal Power Flow (OPF), i. e., machine-learning models that approximate the input/output relationship of OPF.
no code implementations • 18 Aug 2022 • Seonho Park, Pascal Van Hentenryck
This paper takes a different route and proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference.
no code implementations • 26 Sep 2022 • Oliver Stover, Pranav Karve, Sankaran Mahadevan, Wenbo Chen, Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck
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 generation.
no code implementations • 21 Nov 2022 • Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
The remarkable performance of deep learning models and their applications in consequential domains (e. g., facial recognition) introduces important challenges at the intersection of equity and security.
no code implementations • 28 Nov 2022 • Seonho Park, Wenbo Chen, Dahye Han, Mathieu Tanneau, Pascal Van Hentenryck
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors.
no code implementations • 21 Jan 2023 • Seonho Park, Wenbo Chen, Terrence W. K. Mak, Pascal Van Hentenryck
This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA).
no code implementations • 23 Jan 2023 • 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.
no code implementations • 28 Jan 2023 • Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck, Saswat Das, Christine Task
The results show that, contrary to popular beliefs, traditional differential privacy techniques may be superior in terms of accuracy and fairness to differential private counterparts of widely used DA mechanisms.
no code implementations • 27 Feb 2023 • Tejas Santanam, Anthony Trasatti, Hanyu Zhang, Connor Riley, Pascal Van Hentenryck, Ramayya Krishnan
This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown.
no code implementations • 14 Mar 2023 • Nabeel Gillani, Doug Beeferman, Christine Vega-Pourheydarian, Cassandra Overney, Pascal Van Hentenryck, Deb Roy
Most US school districts draw "attendance boundaries" to define catchment areas that assign students to schools near their homes, often recapitulating neighborhood demographic segregation in schools.
no code implementations • 10 Apr 2023 • John Dickerson, Bistra Dilkina, Yu Ding, Swati Gupta, Pascal Van Hentenryck, Sven Koenig, Ramayya Krishnan, Radhika Kulkarni, Catherine Gill, Haley Griffin, Maddy Hunter, Ann Schwartz
This workshop Report Out focuses on the foundational elements of trustworthy AI and OR technology, and how to ensure all AI and OR systems implement these elements in their system designs.
no code implementations • 23 Apr 2023 • 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.
no code implementations • 20 Jun 2023 • Tejas Santanam, Pascal Van Hentenryck
This paper details an outlook on modern constraint programming (CP) education through the lens of a CP instructor.
no code implementations • 5 Jul 2023 • Pascal Van Hentenryck, Kevin Dalmeijer
This article is a short introduction to AI4OPT, the NSF AI Institute for Advances in Optimization.
no code implementations • 8 Jul 2023 • Ritesh Ojha, Wenbo Chen, Hanyu Zhang, Reem Khir, Alan Erera, Pascal Van Hentenryck
The paper also proposes an optimization proxy to address the computational challenges of the optimization models.
no code implementations • 28 Sep 2023 • Hanyu Zhang, Mathieu Tanneau, Chaofan Huang, V. Roshan Joseph, Shangkun Wang, Pascal Van Hentenryck
This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks.
no code implementations • 3 Oct 2023 • Kevin Wu, Mathieu Tanneau, Pascal Van Hentenryck
Transmission Network Expansion Planning (TNEP) problems find the most economical way of expanding a given grid given long-term growth in generation capacity and demand patterns.
no code implementations • 4 Oct 2023 • Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck
In recent years, there has been significant interest in the development of machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF).
no code implementations • 6 Oct 2023 • Andrew Rosemberg, Mathieu Tanneau, Bruno Fanzeres, Joaquim Garcia, Pascal Van Hentenryck
The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints.
no code implementations • 22 Nov 2023 • James Kotary, Vincenzo Di Vito, Jacob Christopher, Pascal Van Hentenryck, Ferdinando Fioretto
This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
no code implementations • 29 Nov 2023 • Seonho Park, Pascal Van Hentenryck
This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems in milliseconds.
no code implementations • 8 Jan 2024 • Jiawei Lu, Tinghan Ye, Wenbo Chen, Pascal Van Hentenryck
AGGNNI-CG also produces significant improvements in service compared to the existing system.
no code implementations • 4 Feb 2024 • Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
This paper introduces Dual Interior Point Learning (DIPL) and Dual Supergradient Learning (DSL) to learn dual feasible solutions to parametric linear programs with bounded variables, which are pervasive across many industries.
no code implementations • 5 Feb 2024 • Mathieu Tanneau, Pascal Van Hentenryck
This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology that combines conic duality theory with the represen- tation power of ML models.