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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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]
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]
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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See all related topics to #cecile-dupouy

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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Supervisors

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]

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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See all related topics to #fulin-yan
Workflow

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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Supervisors

PhD title: Decomposition and aggregation for the short-term hydro unit commitment and scheduling problem in a hydro valley


Collaborations
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.
[Read more]

See all related topics to #lionel-rolland

Ph.D title: Aggregation-disaggregation techniques for solving large network-flow models

Supervisors: François Clautiaux (Edge) and Aurélien Froger (Edge)

Objective of the thesis

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 prcodeoblems in different application fields.

We will jointly study two types of approaches, MIP and SAT, to reach our goals. MIP-based methods are useful to obtain proven optimal solutions, and to produce theoretical guarantees, whereas SAT solvers are strong to detect infeasible solutions and learn clauses to exclude these solutions. Their combination with CP through lazy clause generation is one of the best tools to solve highly combinatorial and non- linear problems. Aggregation/disaggregation techniques generally make use of many sub-routines, which allows an efficient hybridization of the different optimization paradigms. We also expect a deeper cross-fertilization between these different sets of techniques and the different communities.


Publications
Journal articles 24
Preprints (24)
HDR (24)
Thesis (24)
Conferences and workshops (24)
Book chapters (24)
Reports (24)

Projects
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.
[Read more]

New results

See all related topics to #luis-marques

PhD title: Disaster management and robust optimization

Supervisors : Boris Detienne (Edge)



Publications
Journal articles 24
Preprints (24)
HDR (24)
Thesis (24)
Conferences and workshops (24)
Book chapters (24)
Reports (24)

Projects

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.

[Read more]

See all related topics to #parfait-ametana

Supervisors

PhD title: “Routing under uncertainty”

Objective of the thesis

Solving routing problems under uncertainty.


Collaborations

Saint-Gobain Research Paris is an industrial research and development centre working for light and sustainable construction of the Saint-Gobain Group, the world leader in light and sustainable construction.

The collaboration is centered around the PhD thesis of Pierre Pinet.

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See all related topics to #pierre-pinet