2 citations · 2 across the 7 of their papers we have counts for
7 papers
The Branch-and-Bound Tree Closure
Marius Roland, Nagisa Sugishita, Alexandre Forel +3
This paper investigates the a-posteriori analysis of Branch-and-Bound~(BB) trees to extract structural information about the feasible region of mixed-binary linear programs. We int…
DistrictNet: Decision-aware learning for geographical districting
Cheikh Ahmed, Alexandre Forel, Axel Parmentier +1
Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts. In logistics, it is a major strategic decision determining op…
Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles
Youssouf Emine, Alexandre Forel, Idriss Malek +1
Tree ensembles, including boosting methods, are highly effective and widely used for tabular data. However, large ensembles lack interpretability and require longer inference times…
CF-OPT: Counterfactual Explanations for Structured Prediction
Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier +1
Optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipeli…
Combinatorial Optimization and Machine Learning for Dynamic Inventory Routing
Toni Greif, Louis Bouvier, Christoph M. Flath +3
We introduce a combinatorial optimization-enriched machine learning pipeline and a novel learning paradigm to solve inventory routing problems with stochastic demand and dynamic in…
Deep Learning for Data-Driven Districting-and-Routing
Arthur Ferraz, Cheikh Ahmed, Quentin Cappart +1
Districting-and-routing is a strategic problem aiming to aggregate basic geographical units (e.g., zip codes) into delivery districts. Its goal is to minimize the expected long-ter…
Adaptive Partitioning for Chance-Constrained Problems with Finite Support
Marius Roland, Alexandre Forel, Thibaut Vidal
This paper studies chance-constrained stochastic optimization problems with finite support. It presents an iterative method that solves reduced-size chance-constrained models obtai…