Approaching Sustainability in Engineering Design with Multiple Criteria Decision Analysis

This research aimed to the establishment of a general methodological framework, via which the "fuzzy" and "debatable" goal of sustainability can be practically achieved in engineering design. In-depth literature review on the sustainability concept was first conducted in an attempt to grasp its philosophical essence from various interpretations and distinct implementations. The application of the proposed framework was addressed by developing or identifying specific building block techniques, each of which accomplish a different task, such as criteria-attribute mapping, preference modeling, and search. The proposed building block techniques were selected based on systematic comparisons among a wide range of alternative methods and tested by case studies or test problems. Sustainability is a multiplex property of an integrated system. The key to make a reality of sustainability in engineering design is to properly handle its complex nature and deeply rooted conflicts. In this work, Multiple Criteria Decision Analysis (MCDA) was proven ideal for filling the vacuum of a general operational framework. To implement this framework, a four-step procedure needs to be first performed to formulate a sustainability-oriented design into a "standard" Multiple Criteria Decision Making (MCDM) problem. The proposed attribute hierarchy "Stressor-Status-Effect-Integrality-Well-being" and the 4-class metric classification scheme could help engineers to accomplish such a task in the environmental dimension. The achievement of the final "sustainable" design relies on making appropriate decisions. A MAVT-based technique developed in this study provides a rational and informed way of solving the decision problems with a discrete set of explicitly known alternatives. For Multi-Objective Programming (MOP) problems featuring an infinite and implicitly characterized alternative space, the proposed Ordinal Ranking-based Genetic Algorithm (ORGA) offers a desired searching tool by generating uniformly sampled solutions that are feasible and globally Pareto optimal.School of Chemical Engineerin

Contribution à l'interrogation flexible et personnalisée d'objets complexes modélisés par des graphes

Plusieurs domaines d'application traitent des objets et des données complexes dont la structure et la sémantique de leurs composants sont des informations importantes pour leur manipulation et leur exploitation. La structure de graphe a été bien souvent adoptée, comme modèles de représentation, dans ces domaines. Elle permet de véhiculer un maximum d'informations, liées à la structure, la sémantique et au comportement de ces objets, nécessaires pour assurer une meilleure représentation et une manipulation e cace. Ainsi, lors d'une comparaison entre deux objets complexes, l'opération d'appariement est appliquée entre les graphes les modélisant. Nous nous sommes intéressés dans cette thèse à l'appariement approximatif qui permet de sélectionner les graphes les plus similaires au graphe d'une requête. L'objectif de notre travail est de contribuer à l'interrogation exible et personnalisée d'objets complexes modélisés sous forme de graphes pour identi er les graphes les plus pertinents aux besoins de l'utilisateur, exprimés d'une manière partielle ou imprécise. Dans un premier temps, nous avons proposé un cadre de sélection de services Web modélisés sous forme de graphes qui permet (i) d'améliorer le processus d'appariement en intégrant les préférences des utilisateurs et l'aspect structurel des graphes comparés, et (ii) de retourner les services les plus pertinents. Une deuxième méthode d'évaluation de requêtes de recherche de graphes par similarité a également été présentée pour calculer le skyline de graphes d'une requête utilisateur en tenant compte de plusieurs mesures de distance de graphes. En n, des approches de ra nement ont été dé nies pour réduire la taille, souvent importante, du skyline. Elles ont pour but d'identi er et d'ordonner les points skyline qui répondent le mieux à la requête de l'utilisateur.Several application domains deal with complex objects whose structure and semantics of their components are crucial for their handling. For this, graph structure has been adopted, as a model of representation, in these areas to capture a maximum of information, related to the structure, semantics and behavior of such objects, necessary for e ective representation and processing. Thus, when comparing two complex objects, a matching technique is applied between their graph structures. In this thesis, we are interested in approximate matching techniques which constitute suitable tools to automatically nd and select the most similar graphs to user graph query. The aim of our work is to develop methods to personalized and exible querying of repositories of complex objects modeled thanks to graphs and then to return the graphs results that t best the users needs, often expressed partially and in an imprecise way. In a rst time, we propose a exible approach for Web service retrieval that relies both on preference satis ability and structural similarity between process model graphs. This approach allows (i) to improve the matching process by integrating user preferences and the graph structural aspect, and (ii) to return the most relevant services. A second method for evaluating graph similarity queries is also presented. It retrieves graph similarity skyline of a user query by considering a vector of several graph distance measures instead of a single measure. Thus, graphs which are maximally similar to graph query are returned in an ordered way. Finally, re nement methods have been developed to reduce the size of the skyline when it is of a signi cant size. They aim to identify and order skyline points that match best the user query.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Optimisation de forme multi-objectif sur machines parallèles avec méta-modèles et coupleurs. Application aux chambres de combustion aéronautiques

Les normes drastiques sur les émissions d'espèces polluantes et la volonté de réduire les délais de mise sur le marché incitent les motoristes à repenser les concepts de la nouvelle génération de chambre de combustion ainsi que leurs méthodes de conception. Les codes de simulation numérique des écoulements turbulents réactifs, basés sur une approche de moyenne de Reynolds (RANS), sont utilisés depuis quelques années par les ingénieurs dans les phases de conception des foyer aéronautiques. Leur emploi a permis de réduire les temps et les coûts de conception en diminuant notamment le nombre d'essais expérimentaux. La manière d'utiliser ces outils demeure un point clef pour élaborer des environnements d'aide à la décision performants. Le but de ces travaux de thèse est de fournir une méthodologie basée sur des considérations issues de l'optimisation multi- bjectif pour développer un outil de conception automatisé qui intègre des codes de simulation numérique pour évaluer les configurations. En premier lieu, les études rapportées dans ce manuscrit concernent l'automatisation des procédures de simulation en insistant sur les aspects de génération automatique de maillage. Ensuite, le problème des temps de restitution liés à l'utilisation conjointe de techniques d'optimisation et de codes de calcul coûteux en ressources informatiques est adressé en proposant un algorithme basé sur des méta-modèles. L'outil final est construit à partir d'un coupleur de codes parallèles, lui conférant ainsi des caractéristiques intéressantes de performance et de flexibilité. Finalement, après divers tests de validation et d'évaluation, une application sur une chambre de combustion industrielle montre les capacités de la méthode à identifier des configurations prometteuses. ABSTRACT : Drastic norms on pollutant emissions and the need to reduce times to market encourage aeronautical engine manufacturers to reconsider the concepts of the next generation of combustion chamber as well as their design methodologies. Reactive and turbulent simulation codes based on the RANS approach have been used for a few years by engineers in the design cycle of aeronautical combustion chambers. Their use has allowed to reduce development times and costs mostly by decreasing the number of experimental tests. The way to integrate these tools is still a challenging point when the development of an efficient design framework is considered. The aim of this work is to provide a multiobjective optimization based methodology to develop a fully automated tool that evaluates design with simulation codes. First, the studies presented in this report deal with the automation of the simulation processes while insisting on the automatic mesh generation aspects. Then, to reduce the overall response time caused by the use of optimization technics with expensive simulation codes, a strategy based on metamodeling is proposed. The resulting tool is developed with a parallel code coupler offering performance and flexibility to the application. Finally, after some validations and evaluations on test cases, an application on an industrial combustor underlines the capacities of the mehod to identify promising design

Fuzzy Optimality and Evolutionary Multiobjective Optimization

Abstract. Pareto optimality is someway ineffective for optimization problems with several (more than three) objectives. In fact the Pareto optimal set tends to become a wide portion of the whole design domain search space with the increasing of the numbers of objectives. Consequently, little or no help is given to the human decision maker. Here we use fuzzy logic to give two new definitions of optimality that extend the notion of Pareto optimality. Our aim is to identify, inside the set of Pareto optimal solutions, different “degrees of optimality ” such that only a few solutions have the highest degree of optimality; even in problems with a big number of objectives. Then we demonstrate (on simple analytical test cases) the coherence of these definitions and their reduction to Pareto optimality in some special subcases. At last we introduce a first extension of (1+1)ES mutation operator able to approximate the set of solutions with a given degree of optimality, and test it on analytical test cases.