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3. A Large Neighborhood Search Metaheuristic for the Stochastic Mixed Model Assembly Line Balancing Problem with Walking Workers

Author

Thompson, Joseph Orion, Lahrichi, Nadia, Meyer, Patrick, Mohammadi, Mehrdad, Thevenin, Simon, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Sevaux, Marc, editor, Olteanu, Alexandru-Liviu, editor, Pardo, Eduardo G., editor, Sifaleras, Angelo, editor, and Makboul, Salma, editor

Published
2024
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4. Robust design and reconfiguration planning of mixed-model assembly lines under uncertain evolutions of product family.

Author

Mezghani, Yosra, Hashemi-Petroodi, S. Ehsan, Thevenin, Simon, and Dolgui, Alexandre

Subjects
ASSEMBLY line methods, ROBUST optimization, LINEAR programming
Abstract

Assembly lines commonly run for dozens of years before being decommissioned. As product families may evolve several times per year by following the needs of sales and marketing, process engineers reconfigure the lines several dozens of times throughout their life cycle. If the line is not flexible enough, these reconfigurations may be costly, and they can lead to poor efficiency. The present work investigates the possibility of designing a line while accounting for product evolution throughout the life cycle of the line. The evolution of the product family is unknown and we consider a robust optimisation approach. We study a mixed-model assembly line, where each station contains a worker/robot and its equipment. The line produces different product models from the same family, and a reconfiguration occurs when a new product model replaces one of the current variants in the product family. Reconfiguration re-arranges resources and equipment pieces, and it can re-assign some tasks. In this study, we formulate a novel Mixed-Integer Linear Programming (MILP) that minimizes the total cost of the initial design and future reconfigurations of the line over some future product family evolution for the worst case. We consider the worst-case among different scenarios that represent possible production requirements of the new product model. An adversarial approach is also developed to solve large-size instances. We perform computational experiments on the benchmark data from the literature. The results show the proposed adversarial approach performs well, and the proposed robust model significantly reduces the design and reconfiguration costs when compared to the classical approach that designs and reconfigures by accounting only for the current product family. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Makespan estimation in a flexible job-shop scheduling environment using machine learning.

Author

Tremblet, David, Thevenin, Simon, and Dolgui, Alexandre

Subjects
PRODUCTION scheduling, FLOW shop scheduling, MACHINE learning, ARTIFICIAL neural networks, PRODUCTION planning, INDUSTRIAL capacity, DECISION trees, CONSUMERS
Abstract

A production plan gives the quantity of products to release on the shop floor in each period, where a period may represent a week or a month. The plan is the basis for negotiating order acceptance and delivery dates with customers and suppliers. The production plan must respect the available capacity on the shop floor, as underloading the shop floor leads to a loss of opportunity, and thus, a loss of competitiveness for the company. To properly manage the production capacity while negotiating with suppliers and customers, the production planners need a tool to accurately estimate the capacity consumption in each period. The computation of capacity consumption requires creating a detailed production schedule which is a complex task. Algorithms that find close to optimal schedules in a complex manufacturing environment are often time-consuming, which is impractical in a negotiation context. We investigate machine learning models to predict capacity consumption. We consider a flexible job-shop as commonly encountered in practice, and proposed several machine learning models. Namely, several variants of linear regression, decision trees, and artificial neural networks. Numerical experiments showed that our models outperform those found by both an exact approach and dispatching rules when computation time is short. [ABSTRACT FROM AUTHOR]

Published
2024
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19. A state-of-the-art on production planning in Industry 4.0.

Author

Luo, Dan, Thevenin, Simon, and Dolgui, Alexandre

Subjects
PRODUCTION planning, INDUSTRY 4.0, DIGITAL twins, MANUFACTURING processes, INTERNET of things, BLOCKCHAINS
Abstract

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Published
2023
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20. Advancing Circular Economy: Research Roadmap for Circular Integrated Production Systems

Author

Paul, Magdalena, Thevenin, Simon, Schulz, Julia, Brahimi, Nadjib, Benderbal, Hichem Haddou, Dolgui, Alexandre, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Camarinha-Matos, Luis M., editor, Boucher, Xavier, editor, and Afsarmanesh, Hamideh, editor

Published
2021
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21. Optimization for Lot-Sizing Problems Under Uncertainty: A Data-Driven Perspective

Author

Metzker, Paula, Thevenin, Simon, Adulyasak, Yossiri, Dolgui, Alexandre, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Dolgui, Alexandre, editor, Bernard, Alain, editor, Lemoine, David, editor, von Cieminski, Gregor, editor, and Romero, David, editor

Published
2021
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22. A Digital Twin-Driven Methodology for Material Resource Planning Under Uncertainties

Author

Luo, Dan, Thevenin, Simon, Dolgui, Alexandre, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Dolgui, Alexandre, editor, Bernard, Alain, editor, Lemoine, David, editor, von Cieminski, Gregor, editor, and Romero, David, editor

Published
2021
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25. The Impact of Dynamic Tasks Assignment in Paced Mixed-Model Assembly Line with Moving Workers

Author

Hashemi-Petroodi, S. Ehsan, Thevenin, Simon, Kovalev, Sergey, Dolgui, Alexandre, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Lalic, Bojan, editor, Majstorovic, Vidosav, editor, Marjanovic, Ugljesa, editor, von Cieminski, Gregor, editor, and Romero, David, editor

Published
2020
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27. Decision Support System for Joint Product Design and Reconfiguration of Production Systems

Author

Hashemi-Petroodi, S. Ehsan, Gonnermann, Clemens, Paul, Magdalena, Thevenin, Simon, Dolgui, Alexandre, Reinhart, Gunther, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Pras, Aiko, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Furbach, Ulrich, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Ameri, Farhad, editor, Stecke, Kathryn E., editor, von Cieminski, Gregor, editor, and Kiritsis, Dimitris, editor

Published
2019
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36. Workforce reconfiguration strategies in manufacturing systems: a state of the art.

Author

Hashemi-Petroodi, S. Ehsan, Dolgui, Alexandre, Kovalev, Sergey, Kovalyov, Mikhail Y., and Thevenin, Simon

Subjects
MANUFACTURING cells, FLEXIBLE manufacturing systems, LABOR supply, ASSEMBLY line methods, WORKFORCE planning, PRODUCTION planning
Abstract

This paper provides a literature review and an analysis of the studies related to workforce reconfiguration strategies as a part of workforce planning for various production environments. The survey demonstrates that these strategies play a crucial role in the resilience and flexibility of manufacturing systems since they help industrial companies to quickly adapt to frequent changes in demand both in terms of volume and product mix. Five strategies are considered: the use of utility, temporary, walking, cross-trained workers, and bucket brigades. They are analysed in the context of mixed and multi-model manual assembly lines, dedicated, cellular, flexible, and reconfigurable manufacturing systems. The review shows that most of the researches on these reconfiguration strategies focus on multi- or mixed-model assembly lines. At the same time, few studies consider workers team reconfiguration in flexible and reconfigurable manufacturing systems. Finally, this paper reveals several promising research directions in workforce reconfiguration planning, namely, the use of both machine and workforce reconfigurations, consideration of the ergonomic aspects, the combination of multiple workforce reconfiguration strategies, the study of workforce reconfiguration in human-robot collaborative systems, and the use of new technologies in human-machine industrial environments. [ABSTRACT FROM AUTHOR]

Published
2021
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37. Predicting makespan in Flexible Job Shop Scheduling Problem using Machine Learning

Author

Hashemi-Petroodi, S. Ehsan, Thevenin, Simon, Dolgui, Alexandre, Modélisation, Optimisation et DEcision pour la Logistique, l'Industrie et les Services (LS2N - équipe MODELIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut Mines-Télécom [Paris] (IMT), Région Pays de la Loire, and European Project: 101000165,ASSISTANT

Subjects
Control and Systems Engineering, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
Abstract

International audience; Machine learning tools have experienced a growing interest in the early 2010s, providing efficient predictive approaches for artificial intelligence and statistical analysis. These same prediction methods have also sparked interest in the operations research community for decision-making based on predictive analysis by exploiting massive histories and datasets. This study investigates the potential of machine learning tools to predict the feasibility of a production plan. Production schedules are often not able to adhere to the production plans because production plans are built without accounting for all the detailed requirements that arise at the scheduling level. We show that predicting the feasibility of a production plan with a decision tree yields a precision of around 90% versus 70% in the classical capacity constraints considered in planning tools.

Published
2022

38. Apprentissage de contraintes : résumé et application sur un problème de planification

Author

Tremblet, David, Thevenin, Simon, Dolgui, A., Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Modélisation, Optimisation et DEcision pour la Logistique, l'Industrie et les Services (LS2N - équipe MODELIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Nantes Université (Nantes Univ)

Subjects
Machine Learning, Planification et Ordonnancement, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Data Driven methods
Abstract

National audience; Les méthodes de machine learning ont suscité un véritable engouement dans la communauté de recherche opérationnelle, et elles se révèlent très efficaces sur des problèmes d'optimisation basée sur l'analyse prédictive de données. Ces méthodes d'apprentissages trouvent leur intérêt dans le fait de mieux capturer certaines tendances observables au travers de jeux de données, offrant de nouvelles perspectives de résolution lorsqu'elles sont appliquées à des problèmes d'optimisation.Un exemple concret est l'utilisation de méthodes basées sur de la régression pour approcher un paramètre incertain tel que des temps de trajet d'un problème de transport ou la demande d'un problème de planification. D'autres approches consistent en l'utilisation de machine learning pour aiguiller des solveurs de programmation mathématique dans leur prise de décisions, préservant ainsi un temps de calcul précieux lors de la résolution d'un problème. La branche de l'apprentissage machine qui nous intéresse ici concerne la traduction de ces méthodes de machine learning en modèles mathématiques, afin de remplacer des contraintes mathématiques difficiles à modéliser. Si l'approche peut paraître triviale pour régression linéaires classique, elle nécessite davantage d'effort pour traduire d'autres modèles basée sur des arbres de décision ou des réseaux de neurones, et l'utilisation de ces méthodes une fois traduit a suscité un intérêt grandissant dans la communauté de recherche opérationnelle ces dernières années. Dans cette présentation, nous expliquons comment intégrer ces modèles d'apprentissage machine dans un programme linéaire en nombres entiers, et nous présentons les avantages de ce type d'approche pour l'optimisation. Pour illustrer nos propos, nous nous pencherons sur un problème de planification et ordonnancement.

Published
2023

44. Robust Lot-sizing and Supplier Selection under Lead Time Uncertainty

Author

Thevenin, Simon, Ben-Ammar, Oussama, Brahimi, Nadjib, Modélisation, Optimisation et DEcision pour la Logistique, l'Industrie et les Services (LS2N - équipe MODELIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), EuroMov - Digital Health in Motion (Euromov DHM), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Montpellier (UM), ESC Rennes School of Business, and ESC Rennes School of Business (ESC [Rennes])

Subjects
Production and Inventory Systems, [SPI]Engineering Sciences [physics], Robust Optimization
Abstract

book, voir url dans champ "Voir aussi"; International audience; We study the single item lot-sizing problem with supplier selectionand uncertain lead time. We consider the situation where a companyhad preselected a set of suppliers for an item, and these suppliers havedifferent prices, different lead times, but also different reliability. Weaim to provide a robust optimization approach to decide when to order,how much to order, and from which suppliers, in the context of un-certain delivery lead time. We formulate the robust optimization prob-lem with polyhedral budgeted uncertainty sets. This formulation doesnot require assumptions on order crossovers, order splitting, or on thestructure of the demand or lead times. We propose an exact row andcolumn generation algorithm to solve the considered problem, alongwith some enhancements including a fast cut generation procedure. Toimprove the scalability of the approach we propose several heuristics,including a hybrid of the robust counterpart reformulation and row andcolumn generation, and a fix-and-optimize approach in the row andcolumn generation framework. Experimental results show that the fix-and-optimize approach provides good results. Finally, we provide in-sight into the reaction of the decision-maker to unreliable suppliers.One of the conclusions is that in the considered framework, an ex-tremely risk-averse decision-maker selects a single supplier, namelythe most reliable one even if it does not offer the lowest price.

Published
2022

47. Apprentissage automatique de contraintes pour des problèmes de planification de production

Author

Tremblet, David, Thevenin, Simon, Dolgui, Alexandre, Modélisation, Optimisation et DEcision pour la Logistique, l'Industrie et les Services (LS2N - équipe MODELIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), and INSA Lyon

Subjects
Machine Learning, Lot Sizing, Ordonnancement, [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO], [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
Abstract

National audience; Apprentissage automatique de contraintes pour des problèmes de planification de production

Published
2022

48. A Robust Approach for the Joint Lot-Sizing and Supplier Selection

Author

Thevenin, Simon, Ben Ammar, Oussama, Brahimi, Nadjib, Thevenin, Simon, Systèmes Logistiques et de Production (SLP ), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - Faculté des Sciences et des Techniques, Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), ESC Rennes School of Business, Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mots-clés : lot-sizing, robust optimization, supplier selection, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

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