49 citations · 226 across the 61 of their papers we have counts for
69 papers
PACR: Parameter-Optimized AC Power Flow Restoration for AC Feasible DCOPF Dispatch
Michael A. Boateng, Russell Bent, Sidhant Misra +3
The DC optimal power flow is widely used in power system operations because of its computational efficiency and scalability. However, DC dispatches are not guaranteed to satisfy th…
Scheduling and Routing in the Flexible Job Shop with Heterogeneous Transbots and Zoning: A Constraint Programming Approach
Arnovi Moinuddin, El Mehdi Er Raqabi, Pascal Van Hentenryck
Coordinating production and material transfers is increasingly important in modern manufacturing systems equipped with mobile transfer robots, known as transbots. This study consid…
Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
Hongzhao Guan, Beste Basciftci, Pascal Van Hentenryck
Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed demand assumptions. Considering the…
Deep Neural Network-Enhanced Frequency-Constrained Optimal Power Flow with Multi-Governor Dynamics
Fan Jiang, Xingpeng Li, Pascal Van Hentenryck
To ensure frequency security in power systems, both the rate of change of frequency (RoCoF) and the frequency nadir (FN) must be explicitly accounted for in real-time frequency-con…
A Rolling-Space Branch-and-Price Algorithm for the Multi-Compartment Vehicle Routing Problem with Multiple Time Windows
El Mehdi Er Raqabi, Kevin Dalmeijer, Pascal Van Hentenryck
This paper investigates the multi-compartment vehicle routing problem with multiple time windows (MCVRPMTW), an extension of the classical vehicle routing problem with time windows…
The Impact of Shared Autonomous Vehicles in Microtransit Systems: A Case Study in Atlanta
Jason Lu, Tejas Santanam, Hongzhao Guan +5
Microtransit systems represent an enhancement to solve the first- and last-mile problem, integrating traditional rail and bus networks with on-demand shuttles into a flexible, inte…
ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs
Junyang Cai, El Mehdi Er Raqabi, Pascal Van Hentenryck +1
Mixed-Integer Linear Programs (MIPs) are powerful and flexible tools for modeling a wide range of real-world combinatorial optimization problems. Predict-and-Search methods operate…
A Fair OR-ML Framework for Resource Substitution in Large-Scale Networks
Ved Mohan, El Mehdi Er Raqabi, Pascal Van Hentenryck
Ensuring that the right resource is available at the right location and time remains a major challenge for organizations operating large-scale logistics networks. The challenge com…
An Aligned Constraint Programming Model For Serial Batch Scheduling With Minimum Batch Size
Jorge A. Huertas, Pascal Van Hentenryck
In serial batch (s-batch) scheduling, jobs from similar families are grouped into batches and processed sequentially to avoid repetitive setups that are required when processing co…
Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies
Miao Li, Michael Klamkin, Pascal Van Hentenryck +2
This paper studies optimization proxies, machine learning (ML) models trained to efficiently predict optimal solutions for AC Optimal Power Flow (ACOPF) problems. While promising,…
Sequence Variables: A Constraint Programming Computational Domain for Routing and Sequencing
Augustin Delecluse, Pierre Schaus, Pascal Van Hentenryck
Constraint Programming (CP) offers an intuitive, declarative framework for modeling Vehicle Routing Problems (VRP). While classical successor-based CP models can be adapted to hand…
Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analys…
Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization
Dekang Meng, Rabab Haider, Pascal van Hentenryck
This paper introduces OptiGridML, a machine learning framework for discrete topology optimization in power grids. The task involves selecting substation breaker configurations that…
Practice-Based Optimization for the Strategic Locomotive Assignment Problem
Yunji Kim, Amira Hijazi, Kevin Dalmeijer +1
This study addresses the challenge of efficiently assigning locomotives in large freight rail networks, where operational complexity and power imbalances make cost-effective planni…
Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks
Mohammed Faisal Ahmed, Pascal Van Hentenryck, Ahmed El Nashar
This paper introduces the Trailer Path Optimization with Schedule Services Problem (TPOSSP) and proposes a column-generation heuristic (CG-heuristic) to find high-quality solutions…
DualSchool: How Reliable are LLMs for Optimization Education?
Michael Klamkin, Arnaud Deza, Sikai Cheng +2
Consider the following task taught in introductory optimization courses which addresses challenges articulated by the community at the intersection of (generative) AI and OR: gener…
Sobolev Training of End-to-End Optimization Proxies
Andrew W. Rosemberg, Joaquim Dias Garcia, Russell Bent +1
Optimization proxies - machine learning models trained to approximate the solution mapping of parametric optimization problems in a single forward pass - offer dramatic reductions…
Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints
Lucas Kletzander, Tommaso Mannelli Mazzoli, Nysret Musliu +1
The Bus Driver Scheduling Problem (BDSP) is a combinatorial optimization problem with the goal to design shifts to cover prearranged bus tours. The objective takes into account the…
Conformal Predictive Distributions for Order Fulfillment Time Forecasting
Tinghan Ye, Amira Hijazi, Pascal Van Hentenryck
Accurate estimation of order fulfillment time is critical for e-commerce logistics, yet traditional rule-based approaches often fail to capture the inherent uncertainties in delive…
PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planning
Vahid Eghbal Akhlaghi, Reza Zandehshahvar, Pascal Van Hentenryck
This paper considers how to fuse Machine Learning (ML) and optimization to solve large-scale Supply Chain Planning (SCP) optimization problems. These problems can be formulated as…
Tightening optimality gap with confidence through conformal prediction
Miao Li, Michael Klamkin, Russell Bent +1
Decision makers routinely use constrained optimization technology to plan and operate complex systems like global supply chains or power grids. In this context, practitioners must…
Enhanced Renewable Energy Forecasting and Operations through Probabilistic Forecast Aggregation
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
Accurate and reliable forecasting of renewable energy generation is crucial for the efficient integration of renewable sources into the power grid. In particular, probabilistic for…
Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow
Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck
The nonlinear, non-convex AC Optimal Power Flow (AC-OPF) problem is fundamental for power systems operations. The intrinsic complexity of AC-OPF has fueled a growing interest in th…
Optimization Learning
Pascal Van Hentenryck
This article introduces the concept of optimization learning, a methodology to design optimization proxies that learn the input/output mapping of parametric optimization problems.…