A two year engineer position dedicated to 3D robot pallet loading is open within project Gril4all.
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A three-year PhD position at EDF with the Inria center of the University of Bordeaux (EDGE team) is open.
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A three-year PhD position at Saint-Gobain Recherche with the Inria center of the University of Bordeaux (EDGE team) is open.
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A two year postdoc position in our team is open.
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A one year engineer position in our team is open.
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CATIE (Centre Aquitain des Technologies de l’Information et Électroniques) is a non-profit organization created in 2014 based in the Région Nouvelle-Aquitaine. As a technology resources center specialized in digital technology, its main mission is to support SMEs and intermediate size companies in their digital transformation and to help them embracing and integrating related technologies.
Our collaboration with CATIE is about machine learning and optimization. We focus on problems related to shortest path problem with side constraints.
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EDF is the major French multinational electric utility company owned by the French state. Our collaboration is about solving the short-term hydro unit commitment and scheduling problem in a hydro valley using decomposition and aggregation techniques.
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Renault is a French multinational automobile manufacturer established in 1899. The company produces a range of cars and vans and in the past, has manufactured trucks, tractors, tanks, buses/coaches, aircraft and aircraft engines, and autorail vehicles.
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A C++ implementation of the Benders by batch algorithm for two-stage stochastic linear problems.
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A six-month internship at the Inria center of the University of Bordeaux (EDGE team) is open.
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Authors: Fulin Yan, François Clautiaux, Aurélien Froger, Boris Albar
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
In November, we had the first workshop for project ACME et Ecole Nationale des Ponts et Chaussées.
Project ACME is funded by PEPR MOBIDEC. The program aims to mobilize the national research community and transportation ecosystem stakeholders to:
- Understand mobility of goods and peopleand anticipate the mobility of goods and people
- Help collect, structure, and interpret mobility data
- Provide decision-making tools to simulate the impact of public policies and evaluate the relevance of new transport solutions
Our projet studies optimization methods for horizontal collaboration in logistics.
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Authors: Fulin Yan, François Clautiaux, Aurélien Froger, Boris Albar
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Authors: François Clautiaux , Ivana Ljubić
Abstract: Mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a [Read more]
Abstract: Mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a [Read more]
Authors: Luis Marques, François Clautiaux, Aurélien Froger
Abstract: We study the problem of designing a cabinet made up of a set of shelves that contain compartments whose contents slide forward on opening. Considering a set of items candidate to be stored in the cabinet over a given time horizon, the problem is to d [Read more]
Abstract: We study the problem of designing a cabinet made up of a set of shelves that contain compartments whose contents slide forward on opening. Considering a set of items candidate to be stored in the cabinet over a given time horizon, the problem is to d [Read more]
The presentation is about the following submitted paper:
François Clautiaux, Siham Essodaigui, Alain Nguyen, Ruslan Sadykov, Nawel Younes. (2023). Models and algorithms for configuring and testing prototype cars. (hal-04185248)
Authors: François Clautiaux, Siham Essodaigui, Alain Nguyen, Ruslan Sadykov, Nawel Younes
Abstract: In this paper, we consider a new industrial problem, which occurs in the context of the automobile industry. This problem occurs during the testing phase of a new vehicle. It involves determining all the variants of the vehicle to be manufactured in [Read more]
Abstract: In this paper, we consider a new industrial problem, which occurs in the context of the automobile industry. This problem occurs during the testing phase of a new vehicle. It involves determining all the variants of the vehicle to be manufactured in [Read more]
Authors: Xavier Blanchot, François Clautiaux, Aurélien Froger, Manuel Ruiz
Abstract: In this paper, we study how a regulatory constraint limiting a measure of unserved demand, called Loss Of Load Expectation (LOLE), can be incorporated into a strategic version of a stochastic generation and transmission expansion planning problem. Th [Read more]
Abstract: In this paper, we study how a regulatory constraint limiting a measure of unserved demand, called Loss Of Load Expectation (LOLE), can be incorporated into a strategic version of a stochastic generation and transmission expansion planning problem. Th [Read more]
The project:
Project ACME is funded by PEPR MOBIDEC. The program aims to mobilize the national research community and transportation ecosystem stakeholders to:
- Understand mobility of goods and peopleand anticipate the mobility of goods and people
- Help collect, structure, and interpret mobility data
- Provide decision-making tools to simulate the impact of public policies and evaluate the relevance of new transport solutions
Our projet studies optimization methods for horizontal collaboration in logistics.
[Read more]
We consider general aggregation/disaggregation techniques to address optimization problems that are expressed with the help of sequential decision processes. Our main goals are threefold: a generic formalism that encompasses the aforementioned techniques ; more efficient algorithms to control the aggregation procedures ; open-source codes that leverage and integrate these algorithms to efficiently solve hard combinatorial problems in different application fields.
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PhD title: Operational Urban Delivery problem with consolidated parcels and synergized transportation options
Supervisors : Walid Klibli (Kedge BS), François Clautiaux (Edge), Nicolas Labarthe
Seminars
Les systèmes traditionnels de livraison urbaine reposent sur des véhicules, infrastructures et flux dédiés. Avec la croissance de la demande en milieu urbain et à la nécessité de réduire le trafic, l’utilisation des infrastructures du réseau de transport public pour le transport middle-mile des colis apparaît comme une alternative.
Dans cette présentation, nous traitons le problème de planification à court terme pour la livraison urbaine de colis sur le segment middle-mile, en intégrant les opérations de fret au sein des réseaux de transport public. En s’appuyant sur le principe de la consolidation et conteneurisation, nous proposons un modèle PLNE qui optimise conjointement la conteneurisation des colis et le routage des conteneurs sur un réseau multi-lignes et multi-modes. Notre approche repose sur une représentation multi-graphe du réseau, permettant de modéliser précisément les différentes lignes, modes de transport et options de transfert. Deux types de graphes sont construits à partir du réseau de transport public réel: un graphe espace-temps pour les conteneurs et un graphe pour les colis, ajoutant la dimension du conteneur utilisé. Pour valider notre approche, nous évoquerons une étude de cas basée sur des données réelles de la ville de Bordeaux.
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This project aims at proposing theoretical and practical results for hard combinatorial optimization problems in an uncertain environment. These problems have in common the fact that the parameters needed to assess the validity of the solution and compute its cost are unknown. Uncertainty in decision making can be caused by several external factors. The most common are related to stochastic parameters (service demand, time needed for a task, prices, …). Incomplete information can also come from the presence of competitors whose policies are not known to the decision maker.
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- François Clautiaux (Edge)
- Aurélien Froger (Edge)
PhD title: Solving combinatorial optimization problems using hybrid methods that combine machine learning techniques with existing optimization techniques
Objective of the thesis
We wish to develop heuristic approaches based on hybrid methods to solve problems formulated using Sequential decision processes (SDP). One difficulty is that these approaches only have implicit knowledge of the problem to be solved (via oracles for example). When the problem is very large, it is not possible to generate all of the possible states, and the proposed methods must use exploration phases.
Another objective of the thesis is to provide efficient procedures for the techniques which are used in the resolution of SDP based on successive relaxations of the state space: in particular the oracle which allows to choose the states to be aggregated or disaggregated.
Publications
Journal articles 24
Preprints (24)
HDR (24)
Thesis (24)
Conferences and workshops (24)
Book chapters (24)
Reports (24)
Collaborations
CATIE (Centre Aquitain des Technologies de l’Information et Électroniques) is a non-profit organization created in 2014 based in the Région Nouvelle-Aquitaine. As a technology resources center specialized in digital technology, its main mission is to support SMEs and intermediate size companies in their digital transformation and to help them embracing and integrating related technologies.
Our collaboration with CATIE is about machine learning and optimization. We focus on problems related to shortest path problem with side constraints.
[Read more]New results
Authors: Fulin Yan, François Clautiaux, Aurélien Froger, Boris Albar
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Authors: Fulin Yan, François Clautiaux, Aurélien Froger, Boris Albar
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Authors: Ayse N. Arslan, Jeremy Omer, Fulin Yan
Abstract: The kidney exchange problem (KEP) is an increasingly important healthcare management problem in most European and North American countries which consists of matching incompatible patient-donor pairs in a centralized system. Despite the significant pr [Read more]
Abstract: The kidney exchange problem (KEP) is an increasingly important healthcare management problem in most European and North American countries which consists of matching incompatible patient-donor pairs in a centralized system. Despite the significant pr [Read more]
Seminars
Many combinatorial optimization (CO) problems can be formulated as resource-constrained shortest path problems (RCSPPs) on directed acyclic multigraphs, which represent state-transition graphs defined under the dynamic programming (DP) paradigm. The number of vertices, arcs, and resource constraints depends on the size of the original problem instance. This reformulation is NP-hard. Exact methods require high-quality primal bounds to converge efficiently.
In this work, we focus on designing a generic constructive heuristic algorithm that can be applied to any problem once it is formulated as an RCSPP on a directed acyclic multigraph. Recent advances have demonstrated that combining machine learning (ML) with tree-based search strategies can enhance the solution of CO problems. Building on this idea, we propose an ML-enhanced beam search algorithm for RCSPPs. Our ML model leverages graph-based information to score candidate paths for expansion.
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The aim of the Grip4All project is to make industry more competitive by developing a new palletising cell adapted to the severe constraints imposed on the logistics flow when handling mixed products (of varying dimensions and weight) and arranging them on a pallet, without having to sort them manually upstream. This new type of palletising meets a strong demand from a number of sectors, notably mass distribution and the food industry. It meets the demand for handling heterogeneous products without imposing constraints on their packaging, which significantly improves productivity and eliminates tedious human tasks. No similar solution currently exists on the market. The flexible robotics issues addressed will be transposable to other logistics processes in the factory of the future.
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