collaborators

7 papers

cs.LG2026

Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programs

El Mehdi Er Raqabi, Pascal Van Hentenryck

Large-scale optimization problems are often solved repeatedly under similar structural conditions, leading to substantial computational overhead. This occurs in applications such a…

math.OC2026

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…

cs.AI2026

Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches

Tinghan Ye, Arnaud Deza, Ved Mohan +2

Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynami…

math.OC2026

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…

cs.AI2026

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…

math.OC2026

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…