The present Ph.D. thesis addresses the problem of knowledge-based engineering template update in product design. The reuse of design knowledge has become a key asset for the company's competitiveness. Knowledge-based engineering templates allow to store best practices and knowhow via formulas, rules, scripts, etc. This design knowledge can then be reused by instantiating the template. The instantiation results in the creation of an instance of the template in the specified context. In the scope of complex and large products, such as cars or aircrafts, the maintenance of knowledge-based engineering templates is a challenging task. Several engineers from various disciplines work together and make evolve the templates in order to extend their capabilities or to fix bugs. Furthermore, in some cases, the modifications applied to templates should be forwarded to their instances in order that they benefit from the changes. These issues slow down the adoption of template technologies at a large scale within companies. The objective of this work is to propose an approach in order to support engineers in the template update related tasks. In order to address these issues, a process supporting the template update related tasks is defined. Then a framework is proposed that helps design engineers during the template update process by providing a decision support system and a strategy for the update of template instances. The former is a system designed to ease the collaboration between various experts in order to solve template related problems. The latter aims at providing a sequence of updates to follow, in order to forward the templates' modifications to their instances. This sequence is computed with data extracted from models and templates, which are stored in an ontology designed for this purpose. The ontology is used to represent and to infer knowledge about templates, products and their relations. This facilitates the construction of update sequences as it provides an efficient overview of relationships, even implicit ones.

This thesis investigates cost-effective and reliable resource reservation strategies for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require deterministic and guaranteed access to mobile edge computing (MEC) resources, which is typically achieved through individual reservations. Vehicles submit reservation requests to mobile network operator (MNOs), which allocate computation and communication resources based on these requests. Therefore, optimizing the timing and method for vehicles to place reservation requests in both single and multiple MNO scenarios is crucial, especially in dynamic vehicular environments with fluctuating network conditions and limited resources. Efficient reservation requests are essential as vehicles lack complete information about future resource availability and costs. Additionally, real-world uncertainties, such as unpredictable mobility, which influences future reservation times and costs, complicate the design of an optimal reservation strategy. Furthermore, dynamic pricing models employed by MNOs introduce another layer of complexity to the decision-making process for reservation requests, updates, and exchanges due to their impact on market conditions like resource supply and demand. To address the challenges of resource reservation in single MNO scenarios, this thesis proposes an advanced reservation strategy leveraging a batched long short-term memory (LSTM) model. This approach optimizes the timing of reservations, leading to significant cost savings for vehicles. To further minimize update costs, one-shot and multi-shot reservation update strategies are introduced, complemented by the heuristic greedy reservation updates (HGRU) algorithm. For multiple MNO environments, the thesis addresses cost-effective resource selection by comparing prices and network conditions across MNOs. An adaptive Markov decision process (MDP) framework is proposed, incorporating a deep reinforcement learning (DRL) algorithm, specifically dueling deep Q-learning. To enhance learning efficiency, a novel area-wise approach and adaptive MDP closely resembling real-world conditions are introduced. Furthermore, the temporal fusion transformer (TFT) is employed to effectively handle time-dependent data during model training. The multi-phase training approach, involving both synthetic and real-world data, enables the DRL agent to learn from historical data and adapt to real-time observations. Additionally, a multi-objective approach using a double deep Q-learning algorithm is proposed to minimize the cost of reservation updates while ensuring an optimal strategy and reliable provisioning. Finally, this thesis explores the use of blockchain smart contracts to establish a secure, efficient, and transparent resource trading system for vehicular networks. This blockchain-based architecture optimizes reservation costs and addresses trust issues by enabling decentralized, secure, and cost-effective resource trading among vehicles. By leveraging smart contracts, the system ensures transparency and immutability in transactions, fostering trust among participating vehicles. Simulation results demonstrate that the proposed resource reservation algorithms in single MNO environments outperform benchmark reservation schemes, including immediate reservation schemes, in terms of cost minimization and resource utilization efficiency, which also pose challenges for resource guarantee. In multiple MNO environments, the algorithms effectively manage uncertainties and promote competition between MNOs than single MNO scenario potentially impacting guaranteed resource provisioning. Additionally, while the exchange reservation strategies enhance security, the absence of robust security mechanisms could result in unreliable resource requests from providers, posing challenges to guaranteed resource provisioning.

Les travaux de recherche présentés adressent des problèmes de mises à jour de knowledge-based engineering templates dans le cadre de la conception de produits. La réutilisation de connaissances de conception est devenue un avantage clé pour la compétitivité des entreprises. Le savoir faire ainsi que les bonnes pratiques peuvent être stockés au sein de templates par le biais de formules, règles, scripts, etc. Ces connaissances de conception peuvent alors être réutilisées en instanciant le template. L’instanciation résulte en la création d’une instance du template dans le contexte spécifié.Dans le cadre de produit complexes et imposants tels que des voitures ou des avions, la maintenance des templates est une tache ambitieuse. Plusieurs ingénieurs de diverses disciplines travaillent ensemble et font évoluer les templates pour augmenter leurs aptitudes ou pour corriger des problèmes. De plus, dans certains cas, les modifications faites aux templates devraient être appliquées à leurs instances afin qu’elles puissent bénéficier de ces modifications. Ces problèmes ralentissent l’adoption à grande envergure des templates au sein des entreprises. L’objectif de ce travail est de proposer une approche afin d’épauler les ingénieurs dans les tâches relatives à la mise à jour des templates.Pour traiter ces problèmes, un processus adressant les tâches relatives à la mise à jour des templates est défini. Ensuite, un framework est proposé dans le but d’aider les ingénieurs de conception au cours du processus de mise à jour, en fournissant un système d’aide à la décision ainsi qu’une stratégie de mise à jour des instances. Le premier est un système conçu pour faciliter la collaboration entre les différent experts dans le but de résoudre les problèmes liés aux templates. Le second a pour but d’élaborer une séquence de mise à jour à fin d’appliquer les modifications du template à ses instances. La séquence est calculée avec les données extraites à partir des modèles CAD et des templates. Ces données sont stockées dans une ontologie conçue spécialement à cet effet. L’ontologie est utilisée pour représenter et inférer des connaissances sur les templates, les produits et leur relations. Cela facilite la construction des séquences de mises à jour en fournissant une vue d’ensemble sur les relations entre documents, même implicites. / The present Ph.D. thesis addresses the problem of knowledge-based engineering template update in product design. The reuse of design knowledge has become a key asset for the company’s competitiveness. Knowledge-based engineering templates allow to store best practices and knowhow via formulas, rules, scripts, etc. This design knowledge can then be reused by instantiating the template. The instantiation results in the creation of an instance of the template in the specified context. In the scope of complex and large products, such as cars or aircrafts, the maintenance of knowledge-based engineering templates is a challenging task. Several engineers from various disciplines work together and make evolve the templates in order to extend their capabilities or to fix bugs. Furthermore, in some cases, the modifications applied to templates should be forwarded to their instances in order that they benefit from the changes. These issues slow down the adoption of template technologies at a large scale within companies. The objective of this work is to propose an approach in order to support engineers in the template update related tasks. In order to address these issues, a process supporting the template update related tasks is defined. Then a framework is proposed that helps design engineers during the template update process by providing a decision support system and a strategy for the update of template instances. The former is a system designed to ease the collaboration between various experts in order to solve template related problems. The latter aims at providing a sequence of updates to follow, in order to forward the templates’ modifications to their instances. This sequence is computed with data extracted from models and templates, which are stored in an ontology designed for this purpose. The ontology is used to represent and to infer knowledge about templates, products and their relations. This facilitates the construction of update sequences as it provides an efficient overview of relationships, even implicit ones.