La liste complète des publications du Département Génie mathématique et industriel peut être consultée via notre portail HAL-EMSE.
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Nos dernières publications sur l’archive HAL CCSD CNRS
- Interpolating or predicting data is of utmost importance in machine learning, and Gaussian Process Regression is one of the numerous techniques that is often used in practice. This paper considers the case of multi-input and […]
- Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations. The Kriging method involves length-scale hyperparameters whose optimization is essential to obtain an […]
- Planning the energy transition requires decision makers to have an in-depth knowledge about a given territory. To achieve this, data is collected from multiple sources, at multiple scales, with constraints such as privacy policies. Resulting […]
- Bayesian and evolutionary optimization are two fundamental approaches to the global optimization of black-box functions. This talk first consists in an introduction to Bayesian optimization, in particular the TREGO algorithm that has an additional, important, […]
- In an ever-changing business environment, frequent new product development projects are inevitable. Ramp-up as a critical lifecycle phase plays a major role in successful product marketing. This paper provides an updated overview of ramp-up concepts, […]
- Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have […]
- We consider the problem of learning time-consuming functions defined over unordered sets of vectors. Such functions arise frequently, in particular in the context of networks of devices whose number is not fixed and that interact […]
- Reconfigurable manufacturing systems (RMS) emerged in literature during the last two decades with the aim to respond to the rapid increase in product demand and variations. The implementation of such solutions in the industry is […]
- The reconfigurable manufacturing systems aim to efficiently respond to demand changes. One of the key characteristics of these systems is the scalability, i.e. the ability to modify the volume of the throughput in order to […]
- We consider the task of estimating functions from a restricted number of observations where the inputs are in the form of varying-size sets of vectors. A classical method in this expensive functions context is to […]
Nos dernières publications en texte intégral
- Interpolating or predicting data is of utmost importance in machine learning, and Gaussian Process Regression is one of the numerous techniques that is often used in practice. This paper considers the case of multi-input and multi-output data. It proposes a simple Joint Kriging model where common combination weights are applied to all output variables at […]
- Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations. The Kriging method involves length-scale hyperparameters whose optimization is essential to obtain an accurate model and is typically performed using maximum likelihood estimation (MLE). However, for high-dimensional problems, the hyperparameter optimization is problematic […]
- Planning the energy transition requires decision makers to have an in-depth knowledge about a given territory. To achieve this, data is collected from multiple sources, at multiple scales, with constraints such as privacy policies. Resulting data informs about given areas of space without a specific point location. Such is the case of Energy Performance Certificate […]
- Bayesian and evolutionary optimization are two fundamental approaches to the global optimization of black-box functions. This talk first consists in an introduction to Bayesian optimization, in particular the TREGO algorithm that has an additional, important, trust region mechanism. In addition, we discuss the remarkable complementarities between Bayesian and evolutionary optimization: how the problem is modeled, […]
- In an ever-changing business environment, frequent new product development projects are inevitable. Ramp-up as a critical lifecycle phase plays a major role in successful product marketing. This paper provides an updated overview of ramp-up concepts, challenges, and strategies, and makes recommendations for managing ramp-up projects. To this end, theoretical insights from the literature provided valuable […]
- Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS […]
- We consider the problem of learning time-consuming functions defined over unordered sets of vectors. Such functions arise frequently, in particular in the context of networks of devices whose number is not fixed and that interact with each other. A working example is the modeling of a wind farm. Unordered sets of vectors are a mix […]
- Reconfigurable manufacturing systems (RMS) emerged in literature during the last two decades with the aim to respond to the rapid increase in product demand and variations. The implementation of such solutions in the industry is very recent and remains difficult. In this article, an analysis of the industrial requirements and challenges involving four key aspects […]
- The reconfigurable manufacturing systems aim to efficiently respond to demand changes. One of the key characteristics of these systems is the scalability, i.e. the ability to modify the volume of the throughput in order to fit to the demand variability. The design of the RMS has a high impact on its scalability. In the literature, […]
- We consider the task of estimating functions from a restricted number of observations where the inputs are in the form of varying-size sets of vectors. A classical method in this expensive functions context is to approximate the true expensive function with a Gaussian process that relies on semi-definite positive kernels. Varying-size sets of vectors have […]
Nos dernières thèses et HDR
HDR soutenues depuis 2011
Frédéric Grimaud. Un environnement d’aide à la décision pour l’accompagnement des entreprises (PME) à la transformation dans un contexte d’industrie 4.0, 2020
Nos dernières thèses
Amine Melakhsou. Apprentissage statistique et automatique pour la détection des défauts de soudures : application à la fabrication de ballons d’eau chaude sanitaire, 2023
Michel Kamel.Advanced Probabilistic & Statistical/Machine learning Models For Anomaly Detection: Application in Telecommunication Industry, 2023
Stéphanie Bayard. Proposition d’un cadre méthodologique adaptable pour l’évaluation de la performance d’un système de planification de la production : Application à Demand Driven MRP, 2023
Tran‐Vivi Elodie Perrin. Propagation des incertitudes et calibration de simulations numériques pour l’évaluation des coûts liés aux inondations marines, 2021
Serena Finco. Integrating ergonomics principles in assembly design and balancing techniques, 2020
Adrien Spagnol. Synthèse des mesures de risques pour l’optimisation sous incertitudes, 2020
Nicolas Garland. Méta-modélisation introspective pour l’analyse des phénomènes physiques simulés. Formalisation dans le cadre du co-krigeage et intégration algorithmique en optimisation en inversion, 2020
Andrés-Felipe Lopez-Lopera. Modélisation par processus gaussiens sous contraintes d’inégalité, 2019 ⟨NNT : 2019LYSEM020⟩.
David Gaudrie. Optimisation bayésienne multi-objectif en haute dimension, 2019 ⟨NNT : 2019LYSEM026⟩.
Mohammed-Amine Abdous. Conception optimale des systèmes de production avec ergonomie : application aux lignes d assemblage, 2019 ⟨NNT : 2019LYSEM034⟩.
Malek Ben Salem. Sélection de modèle et échantillonnage séquentiel en métamodélisation: Krigeage et au-delà, 2018 ⟨NNT : 2018LYSEM006⟩.
Jean-Charles Croix. Une nouvelle décomposition des éléments aléatoires gaussiens dans les espaces de Banach et application à l’inversion Bayésienne, 2018 ⟨NNT : 2018LYSEM019⟩.
Muhammad Khoirul Khakim HABIBI. Optimisation d’une chaîne logistique inverse avec la prise en compte des processus de désassemblage, 2017 ⟨NNT : 2017LYSEM005⟩.
Christian Serrano Montero. Planification et ordonnancement des activités dans un centre de crossdock international, 2017 ⟨NNT : 2017LYSEM029⟩.
Simon Carladous. Approche intégrée d’aide à la décision basée sur la propagation de l’imperfection de l’information – application à l’efficacité des mesures de protection torrentielle. Université de Lyon, 2017. Français. 〈NNT : 2017LYSEM008〉. 〈tel-01665142〉
Sergey Malyutin. Algorithms and Software for Decision Support in Design of Assembly and Transfer Lines. Université de Lyon, 2016. English. 〈NNT : 2016LYSEM020〉. 〈tel-01665129〉
Esperan Padonou. Statistical Learning on Circular Domains For Advanced Process Control in Microelectronics. Université de Lyon, 2016. English. 〈NNT : 2016LYSEM009〉. 〈tel-01438684v2〉
Fatme Makssoud. Apport de l’optimisation combinatoire pour la reconfiguration des lignes de production
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2014. Français. <NNT : 2014EMSE0742>
Hassan Maatouk. Correspondance entre régression par processus Gaussien et splines d’interpolation sous contraintes linéaires de type inégalité. Théorie et applications.
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2015. Français. <NNT : 2015EMSE0791>
Jana Fruth. Sensitivy analysis and graph-based methods for black-box functions with on application to sheet metal forming.
General Mathematics [math.GM]. Ecole Nationale Supérieure des Mines de Saint-Etienne, 2015. English. <NNT : 2015EMSE0779>
Nicolas Durrande. Étude de classes de noyaux adaptées à la simplification et à l’interprétation des modèles d’approximation. Une approche fonctionnelle et probabiliste.
Mathématiques générales [math.GM]. Ecole Nationale Supérieure des Mines de Saint-Etienne, 2011. Français
Mohand Lounes Bentaha. Conception combinatoire des lignes de désassemblage sous incertitudes
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2014. Français. <NNT : 2014EMSE0758>
Eric Lalot. Analyse des signaux piezométriques et modélisation pour l’évaluation quantitative et la caractérisation des échanges hydrauliques entre aquifères alluviaux et rivières – Application au Rhône.
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2014. Français. <NNT : 2014EMSE0734>
Sergey Kovalev. PROBLÈMES COMBINATOIRES EN CONFIGURATION DES LIGNES DE FABRICATION : ANALYSE DE COMPLEXITÉ ET OPTIMISATION
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2012. Français. <NNT : 2012EMSE0672>
Jordan Re-Bahuaud. Apports d’une approche couplée physique et géochimie des eaux souterraines pour caractériser les fonctionnements hydrodynamiques transitoires. Cas de la plaine alluviale du Forez.
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2012. Français. <NNT : 2012EMSE0666>
Matthieu Canaud. Estimation de paramètres et planification d’expériences adaptée aux problèmes de cinétique – Application à la dépollution des fumées en sortie des moteurs
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2011. Français. <NNT : 2011 EMSE 0619>
Bertrand Gauthier. Approche spectrale pour l’interpolation à noyaux et positivité conditionnelle
École Nationale Supérieure des Mines de Saint-Étienne, 2011. Français. <NNT : 2011 EMSE 0615>
Mamy Anjara Herisolo Tombozafy. Méthodologie d’analyse de l’enfoncement du lit mineur d’un fleuve : approche combinée modélisation hydraulique-géochimie. Application à la Loire Forézienne (France)
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2011. Français. <NNT : 2010EMSE0596>