Le travail que nous présentons dans cette thèse porte sur le problème d'affectation dans une place de marché de prospects Internet. Plus précisément, ce travail a pour ambition de répondre à la problématique de l'adéquation de l'offre et de la demande, dans un contexte caractérisé par des flux continus faisant évoluer en temps réel l'ensemble des offres disponibles et les demandes à satisfaire. Pour ce faire, nous proposons dans un premier temps un modèle mono-période qui optimise le problème d'affectation à un instant donné et en considérant une seule période de temps, tout en permettant la prise en compte instantanée des nouvelles offres et demandes et leur adéquation en temps réel. Ce modèle permet d'optimiser deux objectifs à savoir: la maximisation du chiffre d'affaires et la satisfaction des clients.Par la suite nous proposons d'étendre ce modèle sur plusieurs périodes de temps futures afin de prendre en compte l'aspect temps réel de l'activité de la place de marché et donc le fait que des flux continus font évoluer en temps réel l'ensemble des offres et des demandes. L'objectif étant de tirer profit de la connaissance concernant cette évolution, par le biais de l'intégration d'un modèle de prévision dans un modèle d'optimisation multi-période.Ainsi, nous proposons un modèle d'optimisation multi-période permettant d'envisager à un instant donné des affectations sur plusieurs périodes de temps futures afin de réaliser les meilleures affectations possibles. Aussi, nous proposons un modèle de prévision des nouveaux flux tout en considérant les caractéristiques du modèle d'optimisation multi-période.Construire un modèle de prévision nécessite de définir les données à prévoir avant d'envisager toute méthode de prévision. En d'autres termes, nous devons choisir les paramètres du modèle de prévision, à savoir: les données historiques appropriées, le pas de temps de la prévision ainsi que l'horizon de la prévision. Le défi consiste donc à définir les paramètres du modèle de prévision qui conviendront au fonctionnement du modèle de l'optimisation multi-période.Par ailleurs, une des caractéristiques de la place de marché est la temporalité de son système. Ainsi, nous proposons un algorithme assurant l'aspect temps réel et donc le fait que les affectations s'effectuent toutes les minutes. L'algorithme que nous proposons fonctionne de manière continue à longueur de journée en optimisant à chaque instant l'adéquation offre/demande de prospects Internet tout en considérant instantanément les flux continus de prospects Internet ainsi que la mise à jour régulière de la demande Enfin, pour mettre en évidence l'efficacité et les bénéfices que la place de marché peut en tirer par l'utilisation des modèles et de l'algorithme proposés, nous avons mené des tests et différentes expérimentations sur des données réelles. Ces tests nous ont permis de valider nos travaux et d'évaluer la qualité des résultats obtenus.L'objectif de ce travail est double, d'une part, donner un cadre solide et formel pour répondre à la problématique de la place de marché de prospects Internet. D'autre part, le cadre proposé devrait être aussi générique que possible afin de résoudre tout autre problème analogue à celui de la place de marché de prospects Internet. / The work that we present in this thesis focuses on the assignment problem in a marketplace of Internet prospects. More precisely, this work aims to address the problem of matching offers and demands in a context characterized by a continuous flows. These latter evolve inreal time the set of available offers and demands to satisfy. To do this, we propose initially a mono-period model which optimizes the assignment problem at a given instant and taking into account asingle period of time while allowing the instantaneous consideration of new offers and demands and their adequacy in real time. This model considers two objectives to optimize, namely: maximization of turnover as well as clients satisfaction.Thereafter, we propose to extend this model over several future time periods in order to take into account the real time aspect of the marketplace activity and so the fact that a continuous flows evolve in real time the set of offers en demands. The objective is to take advantage of knowledge about this evolution, through the integration of a forecasting model in a multi-period optimization model. Thus,we propose a multi-period optimization model for considering at agiven instant assignments over several future time periods. Also, we propose a forecasting model for new flows while considering the characteristics of the multi-period optimization model.Building a forecasting model requires defining the data before considering any forecasting method. In other words, we have to choose the parameters of the forecasting model, namely the appropriate historical data, the forecasting time step and the forecasting horizon. The challenge is to define the parameters of the forecasting model which agree with the functioning the multi-period optimization model.Furthermore, a feature of the marketplace is the temporality of its system. Thus, we propose an algorithm ensuring real-time aspect and so the fact that assignments are made every minute. The proposed algorithm works continuously all day long while optimizing every instant the offer/demand adequacy of Internet prospects and instantly considering the continuous flux of Internet prospects as well as the regular updating demand. Finally, in order to show the efficiency and the benefits that the marketplace can reap by the use of the proposed models, we conducted tests and various experiments on real data. These tests have allowed us to validate the proposed models and evaluate the quality of the results.The aim is twofold, giving a strong and formal framework to address the issue of the marketplace of Internet prospects but also proposing a generic framework to solve any problem similar to that of the marketplace of Internet prospects.

Made available in DSpace on 2016-06-02T19:50:16Z (GMT). No. of bitstreams: 1 4516.pdf: 2685213 bytes, checksum: 549d36e8c309231a3650ebff250bb1af (MD5) Previous issue date: 2012-06-01 / The domain of logistics is concerned with providing customers with the right product in the right place at the right time. In our modern economy, the faster pace and wider scope of logistic operations has led to complex management problems that have drawn the attention of both industry and the academic world Optimizing the number of vehicles for a determined transport system requires a trade-off between the cost of vehicle acquisition and maintenance and the penalties involved in not meeting the requirements of the system. This thesis proposes to contribute to decision making in the operational management of those companies working in the transportation of goods by road, particularly as regards the optimization of vehicle use in freight transfer between terminals. Various operational problems, especially management of the transfer fleet, involves the dynamic allocation of limited resources to meet demand. Specifically, this paper deals with the dynamic (multi-period) vehicle allocation problem (DVAP) in the road transportation of full loads between terminals. The DVAP belongs to that class of problems dealing with dynamic resource allocation and consists of defining the movements of a fleet of vehicles that transport goods between terminals with a wide geographical distribution and which interact among themselves. These movements may be of fully-laden vehicles, unladen vehicles for repositioning or vehicles held at a terminal to meet future demands. Emphasis is given to the characterization of the problem in real situations, mathematical modeling of the problem and the use of operational research techniques in solving the problem. Also, heuristics and metaheuristics such as GRASP, simulated annealing and ant colony optimization are used in the solution. The objective is to determine dynamic allocation and fleet needs in order to minimize operational costs in meeting the demand for services. The main reason for undertaking this work was the possibility of practical application, the development of integer linear programming models and both exact and heuristic methods for solutions, aiming at the practical validation of the approaches in the real operational environment of a Brazilian transport company. / O domínio das atividades logísticas é fornecer aos clientes de um sistema o produto certo, no local certo e no tempo certo. Na economia moderna, o passo acelerado e o grande escopo das operações logísticas tem fomentado problemas gerenciais complexos, atraindo a atenção da indústria e da academia. Otimizar a quantidade de veículos para um determinado sistema de transporte requer a avaliação do tradeoff entre o custo de aquisição e manutenção de veículos e penalidades associadas com o não atendimento de demandas neste sistema. Esta tese se propõe a contribuir para apoiar decisões na gestão operacional de frotas de empresas prestadoras de serviços de transporte rodoviário de cargas. Em particular, na otimização do uso de veículos nos transportes de transferências de cargas entre terminais, tendo como fator crítico e determinante a maximização da utilização dos recursos nas operações. Vários problemas operacionais, em especial o gerenciamento da frota de transferência, consistem em dinamicamente alocar recursos limitados às requisições de tarefas. Especificamente, este trabalho trata do problema da alocação dinâmica (multi-períodos) de veículos (PADV) no transporte rodoviário de cargas completas entre terminais. O PADV pertence a classe de problemas de alocação dinâmica de recursos e consiste em definir movimentos de uma frota de veículos que realiza viagens entre terminais geograficamente dispersos que interagem entre si. Estes movimentos podem ser: veículos carregados com carga completa, veículos vazios para reposicionamento, ou veículos mantidos em um terminal de um período para outro como provisão para o atendimento de demandas futuras. A ênfase é dada na caracterização do problema em situações reais, na modelagem matemática do problema e na solução do mesmo utilizando técnicas de pesquisa operacional, envolvendo ainda a utilização de heurísticas e metaheurísticas para solução, como o GRASP, o simulated annealing e a colônia de formigas. O objetivo é definir a alocação dinâmica e necessidades de frota que minimizem o custo operacional no atendimento a demandas por serviços. A principal motivação para o desenvolvimento do trabalho é a possibilidade de aplicação prática, no desenvolvimento de modelos de programação linear inteira e métodos exatos e heurísticos para as suas soluções, visando a validação prática das abordagens em um ambiente real de operação de uma empresa transportadora no Brasil.

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank.