2 citations · 2 across the 5 of their papers we have counts for
6 papers
Combinatorial Optimization Augmented Machine Learning
Maximilian Schiffer, Heiko Hoppe, Yue Su +2
Combinatorial optimization augmented machine learning (COAML) has recently emerged as a powerful paradigm for integrating predictive models with combinatorial decision-making. By e…
Structured Reinforcement Learning for Combinatorial Decision-Making
Heiko Hoppe, Léo Baty, Louis Bouvier +2
Reinforcement learning (RL) is increasingly applied to real-world problems involving complex and structured decisions, such as routing, scheduling, and assortment planning. These s…
Primal-dual algorithm for contextual stochastic combinatorial optimization
Louis Bouvier, Thibault Prunet, Vincent Leclère +1
This paper introduces a novel approach to contextual stochastic optimization, integrating operations research and machine learning to address decision-making under uncertainty. Tra…
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…
Solving a Continent-Scale Inventory Routing Problem at Renault
Louis Bouvier, Guillaume Dalle, Axel Parmentier +1
This paper is the fruit of a partnership with Renault. Their backward logistic requires solving a continent-scale multi-attribute inventory routing problem (IRP). With an average o…
Learning with Combinatorial Optimization Layers: a Probabilistic Approach
Guillaume Dalle, Léo Baty, Louis Bouvier +1
Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful tool to tackle data-driven decision tasks, but they come with two main challenges. First, t…