Coupling system design and project planning: discussion on a bijective link between system and project structures

This article discuss the architecture of an integrated model able to support the coupling between a system design process and a project planning process. The project planning process is in charge of defining, planning and controlling the system design project. A benchmarking analysis carried out with fifteen companies belonging to the world competitiveness cluster, Aerospace Valley, has highlighted a lack of models, processes and tools for aiding the interactions between the two environments. We define the coupling as the establishment of links between entities of the two domains while preserving their original semantic, thus allowing information to be collected. The proposed coupling is recursive. It enables systems to be decomposed into subsystems when designers consider complexity to be too high, and can also decompose projects into sub-projects. The coupling enables systematically links to be drawn between project entities and system entities. In this paper, we discuss the different possibilities of linking system and project structures during the design and the planning processes. Firstly, after presenting the results of the industrial analysis, the different entities are defined and the various coupling modes are discussed

Diffusive limit approximation of pure jump optimal ergodic control problems

Motivated by the design of fast reinforcement learning algorithms, we study the diffusive limit of a class of pure jump ergodic stochastic control problems. We show that, whenever the intensity of jumps is large enough, the approximation error is governed by the H{\"o}lder continuity of the Hessian matrix of the solution to the limit ergodic partial differential equation. This extends to this context the results of [1] obtained for finite horizon problems. We also explain how to construct a first order error correction term under appropriate smoothness assumptions. Finally, we quantify the error induced by the use of the Markov control policy constructed from the numerical finite difference scheme associated to the limit diffusive problem, this seems to be new in the literature and of its own interest. This approach permits to reduce very significantly the numerical resolution cost

Near-continuous time Reinforcement Learning for continuous state-action spaces

We consider the Reinforcement Learning problem of controlling an unknown dynamical system to maximise the long-term average reward along a single trajectory. Most of the literature considers system interactions that occur in discrete time and discrete state-action spaces. Although this standpoint is suitable for games, it is often inadequate for mechanical or digital systems in which interactions occur at a high frequency, if not in continuous time, and whose state spaces are large if not inherently continuous. Perhaps the only exception is the Linear Quadratic framework for which results exist both in discrete and continuous time. However, its ability to handle continuous states comes with the drawback of a rigid dynamic and reward structure. This work aims to overcome these shortcomings by modelling interaction times with a Poisson clock of frequency $\varepsilon^{-1}$, which captures arbitrary time scales: from discrete ($\varepsilon=1$) to continuous time ($\varepsilon\downarrow0$). In addition, we consider a generic reward function and model the state dynamics according to a jump process with an arbitrary transition kernel on $\mathbb{R}^d$. We show that the celebrated optimism protocol applies when the sub-tasks (learning and planning) can be performed effectively. We tackle learning within the eluder dimension framework and propose an approximate planning method based on a diffusive limit approximation of the jump process. Overall, our algorithm enjoys a regret of order $\tilde{\mathcal{O}}(\varepsilon^{1/2} T+\sqrt{T})$. As the frequency of interactions blows up, the approximation error $\varepsilon^{1/2} T$ vanishes, showing that $\tilde{\mathcal{O}}(\sqrt{T})$ is attainable in near-continuous time

Vers un couplage des processus de conception de systèmes et de planification de projets : formalisation de connaissances méthodologiques et de connaissances métier

Les travaux présentés dans cette thèse s'inscrivent dans une problématique d'aide à la conception de systèmes, à la planification de leur projet de développement et à leur couplage. L'aide à la conception et à la planification repose sur la formalisation de deux grands types de connaissances : les connaissances méthodologiques utilisables quel que soit le projet de conception et, les connaissances métier spécifiques à un type de conception et/ou de planification donné. Le premier chapitre de la thèse propose un état de l'art concernant les travaux sur le couplage des processus de conception de systèmes et de planification des projets associés et expose la problématique de nos travaux. Deux partie traitent ensuite, d'une part, des connaissances méthodologiques et, d'autre part, des connaissances métier. La première partie expose trois types de couplages méthodologiques. Le couplage structurel propose de formaliser les entités de conception et de planification puis permet leur création et leur association. Le couplage informationnel définit les attributs de faisabilité et de vérification pour ces entités et synchronise les états de ces dernières vis-à-vis de ces attributs. Enfin, le couplage décisionnel consiste à proposer, dans un même espace et sous forme de tableau de bord, les informations nécessaires et suffisantes à la prise de décision par les acteurs du projet de conception. La seconde partie propose de formaliser, d'exploiter et de capitaliser la connaissance métier. Après avoir formalisé ces connaissances sous forme d'une ontologie de concepts, deux mécanismes sont exploités : un mécanisme de réutilisation de cas permettant de réutiliser, en les adaptant, les projets de conception passés et un mécanisme de propagation de contraintes permettant de propager des décisions de la conception vers la planification et réciproquement. ABSTRACT : The work presented in this thesis deals with aiding system design, development project planning and its coupling. Aiding design and planning is based on the formalization of two kind of knowledge: methodological knowledge that can be used in all kind of design projects and business knowledge that are dedicated to a particular kind of design and/or planning. The first chapter presents a state of the art about coupling system design process and project planning process and gives the problem of our work. Then, two parts deal with design and planning coupling thanks to, on one hand, methodological knowledge, and on the other hand, business knowledge. The first part presents three types of methodological coupling. The structural coupling defines design and planning entities and permits its simultaneous creation of and its association. The informational coupling defines feasibility and verification attributes for these entities and synchronizes its attribute states. Finally, the decisional coupling consists in proposing, in a single dashboard, the necessary and sufficient information to make a decision by the design project actors. The second part proposes to formalize, to exploit and to capitalize business knowledge. This knowledge is formalized with ontology of concepts. Then, two mechanisms are exploited: a case reuse mechanism that permits to reuse and adapt former design projects and a constraint propagation mechanism that allows propagating decisions from design to planning and reciprocally