8 citations · 31 across the 20 of their papers we have counts for
23 papers
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…
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…
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows
Léo Baty, Kai Jungel, Patrick S. Klein +2
With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same…
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
Kai Jungel, Axel Parmentier, Maximilian Schiffer +1
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and tr…
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Alexandre Forel, Axel Parmentier, Thibaut Vidal
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is…
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…
Future memories are not needed for large classes of POMDPs
Victor Cohen, Axel Parmentier
Optimal policies for partially observed Markov decision processes (POMDPs) are history-dependent: Decisions are made based on the entire history of observation. Memoryless policies…
Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles
Alexandre Forel, Axel Parmentier, Thibaut Vidal
Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Obtaining robust counterfactual explanations is essential…
Learning structured approximations of combinatorial optimization problems
Axel Parmentier
Machine learning pipelines that include a combinatorial optimization layer can give surprisingly efficient heuristics for difficult combinatorial optimization problems. Three quest…
Optimal Counterfactual Explanations in Tree Ensembles
Axel Parmentier, Thibaut Vidal
Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. The absence of guarantees of performance and robustness…
Electric Vehicle Fleets: Scalable Route and Recharge Scheduling through Column Generation
Axel Parmentier, Rafael Martinelli, Thibaut Vidal
The rise of battery-powered vehicles has led to many new technical and methodological hurdles. Among these, the efficient planning of an electric fleet to fulfill passenger transpo…
Learning to solve the single machine scheduling problem with release times and sum of completion times
Axel Parmentier, Vincent T'Kindt
In this paper, we focus on the solution of a hard single machine scheduling problem by new heuristic algorithms embedding techniques from machine learning field and scheduling theo…
Integer programming for weakly coupled stochastic dynamic programs with partial information
Victor Cohen, Axel Parmentier
This paper introduces algorithms for problems where a decision maker has to control a system composed of several components and has access to only partial information on the state…
Two generalizations of Markov blankets
Victor Cohen, Axel Parmentier
In a probabilistic graphical model on a set of variables , the Markov blanket of a random vector is the minimal set of variables conditioned to which is independent from…
Integer programming on the junction tree polytope for influence diagrams
Axel Parmentier, Victor Cohen, Vincent Leclère +2
Influence Diagrams (ID) are a flexible tool to represent discrete stochastic optimization problems, including Markov Decision Process (MDP) and Partially Observable MDP as standard…
Linear Programming for Decision Processes with Partial Information
Victor Cohen, Axel Parmentier
Markov Decision Processes (MDPs) are stochastic optimization problems that model situations where a decision maker controls a system based on its state. Partially observed Markov d…
Ground staff shift planning under delay uncertainty at Air France
Julie Poullet, Axel Parmentier
Ground staff agents of airlines operate many jobs at airports such as passengers check-in, planes cleaning, etc. Shift planning aims at building the sequences of jobs operated by g…
Resource constrained shortest path algorithm for EDF short-term thermal production planning problem
Markus Kruber, Axel Parmentier, Pascal Benchimol
Unit commitment problem on an electricity network consists in choosing the production plan of the plants (units) of a company in order to meet demand constraints. It is generally s…
Aircraft routing and crew pairing: updated algorithms at Air France
Axel Parmentier, Frédéric Meunier
Aircraft routing and crew pairing problems aims at building the sequences of flight legs operated respectively by airplanes and by crews of an airline. Given their impact on airlin…
Algorithms for Non-Linear and Stochastic Resource Constrained Shortest Paths
Axel Parmentier
Resource constrained shortest path problems are usually solved thanks to a smart enumeration of all the non-dominated paths. Recent improvements of these enumeration algorithms rel…
Stochastic Shortest Paths and Risk Measures
Axel Parmentier, Frédéric Meunier
We consider three shortest path problems in directed graphs with random arc lengths. For the first and the second problems, a risk measure is involved. While the first problem cons…