Clautiaux

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.

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.

Description

Repository containing a C++ implementation of the Benders by batch algorithm described in the article:

Xavier Blanchot, François Clautiaux, Boris Detienne, Aurélien Froger, Manuel Ruiz. (2023). The Benders by batch algorithm: design and stabilization of an enhanced algorithm to solve multicut Benders reformulation of two-stage stochastic programs. European Journal of Operational Research. DOI: 10.1016/j.ejor.2023.01.004

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)

PhD title: Operational Urban Delivery problem with consolidated parcels and synergized transportation options

Supervisors : Walid Klibli (Kedge BS), François Clautiaux (Edge), Nicolas Labarthe

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.

Supervisors

• 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

Collaborations

CATIE

Machine learning and optimization

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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New results

KidneyExchange.jl: A Julia package for solving the kidney exchange problem with branch-and-price

 January 19, 2024  |  

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]

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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.

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

Projects

ADLib

A library for Aggregation/Disaggregation of large network flow models

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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New results

Mathematical models based on decision hypergraphs for designing a storage cabinet

 June 6, 2024  |  

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]

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PhD title: Disaster management and robust optimization

Supervisors : Boris Detienne (Edge)

• Olga Battaia (Kedge BS), François Clautiaux (Edge), Mehdi Amiri-Ahref

Publications

Projects

DeSide

Design of spatio-temporal networks in stochastic and dynamic environment: new mathematical models and optimization approaches

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]

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Supervisors

• François Clautiaux (Edge)
• Ayse Arslan (Edge)

PhD title: “Routing under uncertainty”

Objective of the thesis

Solving routing problems under uncertainty.

Collaborations

Saint Gobain

Routing problem under uncertainty

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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The project:

Strategic Power Systems Development for the Future (PowerDev), funded by PEPR TASE, studies optimization methods and reliability/resilience engineering applied to large-scale electrical power systems. The project is led by CentraleSupélec at the University of Paris Saclay and is composed of a consortium of higher education institutions across France (CentraleSupelec, UVSQ, University Grenoble Alpes), as well as research organizations (Inria, CNRS).

Research topic and objectives:

Modern power systems are expected to become increasingly complex to design and operate due to the growing number of renewable energy sources (RES). Renewable energy generation is, by nature, intermittent and introduces an amount of uncertainty that severely affects the physical responses of the power system, particularly in terms of voltage control and frequency regulation [1]. Moreover, RES integration within the power system requires the introduction of many new power electronic devices, which add to the system’s complexity and increase its possible failure modes [2,3]. Combined with unexpected initiating events, these two main features can lead to cascading failure risks, triggering disastrous consequences to the power grid and, most notably, large-scale blackouts [4-7]. The economic and societal consequences to the impacted regions are usually massive, with economic loss measured in the tens of billions of dollars [8]. The main objective of this project is to evaluate and optimize the resilience of power systems in the context of a massive insertion of renewable energies. The project aims to elaborate a comprehensive and integrated set of decision support tools by considering extreme events in present and future climates, the complexity of the power grid, and socio-economic scenarios.

RTE is France’s Transmission System Operator. It is in charge of the high and ultra-high voltage electricity transmission network in France and of the electricity exchanges with the neighbouring countries. Its main role is to guarantee in real time the balance between electricity production and consumption.

In 2019, a contract was signed with RTE for a PhD on algorithms to speedup Benders’ decomposition. The PhD student was Xavier Blanchot under the supervision of François Clautiaux and Aurélien Froger.

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 was centered around the PhD thesis of Quentin Viaud.