One of the financial risks an agent has to deal with is market risk. Market risk is caused by the uncertainty attached to asset values. There exit various measures trying to model market risk. The most widely accepted one is Value-at- Risk. However Value-at-Risk does not encourage portfolio diversification in general, whereas a consistent risk measure has to do so. In this work, risk measures satisfying these consistency conditions are examined within theoretical basis. Different types of coherent and convex risk measures are investigated. Moreover the extension of coherent risk measures to multiperiod settings is discussed.

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.

Neste trabalho, deriva-se uma política de escolha ótima baseada na análise de média-variância para o Erro de Rastreamento no cenário Multi-período - ERM -. Referindo-se ao ERM como a diferença entre o capital acumulado pela carteira escolhida e o acumulado pela carteira de um benchmark. Assim, foi aplicada a metodologia abordada por Li-Ng em [24] para a solução analítica, obtendo-se dessa maneira uma generalização do caso uniperíodo introduzido por Roll em [38]. Em seguida, selecionou-se um portfólio do mercado de ações brasileiro baseado no fator de orrelação, e adotou-se como benchmark o índice da bolsa de valores do estado de São Paulo IBOVESPA, além da taxa básica de juros SELIC como ativo de renda fixa. Dois casos foram abordados: carteira composta somente de ativos de risco, caso I, e carteira com um ativo sem risco indexado à SELIC - e ativos do caso I (caso II). / In this work, an optimal policy for portfolio selection based on mean-varian e analysis for the multi-period tracking error - ERM - was derived. ERM is understood as the difference between the capital raised by the selected portfolio and benchmark portfolio. Thus, the methodology discussed by Li-Ng in [24] for analytical solution was applied, generalizing the single period case introduced by Roll in [38]. Then, it was selected a portfolio from the Brazilian stock trading based on the correlation factor, and adopted as benchmark the index of the stock trading of São Paulo State IBOVESPA, and the basic interest rate SELIC as fixed income asset. Two cases were dealt: portfolio composed of risky assets only, case I, and portfolio with a risk-free asset - indexed to SELIC - and assets of the case I (case II).

Neste trabalho, estudamos o problema de corte bidimensional multiperíodo com sobras aproveitáveis, que consiste em cortar objetos grandes visando a produção de um conjunto de itens menores. Supomos um horizonte de planejamento finito com uma quantidade finita de períodos entre os tempos inicial e final. Primeiramente consideramos uma versão determinística em que conhecemos, à priori, os itens solicitados em uma ordem de trabalho e o custo dos objetos a cada período. Algumas das sobras geradas durante o processo de corte dos itens solicitados em um período podem ser utilizadas como objetos no futuro. As sobras que podem ser usadas no futuro são denominadas sobras aproveitáveis. De forma geral, uma sobra é considerada aproveitável se possui dimensões iguais ou superiores as de algum item de uma lista pré-definida para o período. O objetivo é minimizar o custo total dos objetos utilizados para satisfazer a ordem de trabalho dos itens solicitados de todo o horizonte considerado. Havendo soluções com o mesmo custo, desejamos encontrar aquela que, no fim do horizonte de tempo considerado, maximize o valor das sobras aproveitáveis remanescentes. Apresentamos uma modelagem matemática do problema usando uma formulação em dois níveis, que é transformada em um modelo de programação linear inteira mista, devido às características do problema. Considerando a dificuldade em resolver o modelo desenvolvido, apresentamos uma proposta de uma abordagem heurística baseada em Programação Dinâmica Aproximada (PDA) para lidar com o problema proposto. Outras opções baseadas em estratégias do tipo horizonte rolante e relax-and-fix também são consideradas. Consideramos também o cenário onde não conhecemos de antemão os itens da ordem de trabalho e o custo dos objetos, mas temos informações das distribuições de probabilidade de ambos. Nesse caso, apresentamos uma abordagem baseada em programação dinâmica aproximada para estimar a melhor estratégia a ser seguida em cada período. Comparamos os resultados obtidos pela PDA com os resultados encontrados por um método guloso. Em cenários adequados, os resultados mostram que a PDA consegue soluções superiores ao método guloso. / In this research, we study the multi-period two-dimensional cutting problem with usable leftover, which consists of cutting objects to produce a set of items. We assume a finite planning horizon with a finite amount of periods between the initial and final times. First we consider a deterministic version in which we know, a priori, the set of ordered items and the cost of the objects at each period. Some of the leftovers generated during the cutting process of the ordered items in a period may be used as objects in the future. The leftovers that can be used in the future are called usable leftovers. In general, a leftover is considered usable if it has dimensions equal to or greater than that of some item from a predefined list for the period. The goal is to minimize the total cost of the objects used to cut the set of ordered items of the entire considered horizon. If there are solutions with the same cost, we wish to find one that, at the end of the considered time horizon, maximizes the value of the remaining usable leftovers. We present a mathematical model of the problem using a bilevel formulation, which is transformed into a mixed integer linear programming model, due to the characteristics of the problem. Considering the difficulty in solving the developed model, we propose a heuristic approach based on approximate dynamic programming (ADP) to deal with the proposed problem. Other options based on the rolling horizon and relax-and-fix strategies are also considered. We also consider the scenario where we do not know in advance the set of ordered items and the cost of the objects, but we have information about the probability distributions of both. In this case, we present an approach based on approximate dynamic programming to estimate the best strategy to be followed at each period. We compared the results obtained by the ADP with the results found by a greedy method. In suitable scenarios, the results show that the ADP achieves superior solutions to the greedy method.

La performance logistique des entreprises et l’optimisation des transports sont devenues un grand problème ces dernières années. La planification et l’optimisation des services constituent en particulier un nouveau défi. Afin d’accroître la productivité et de réduire les coûts de la logistique, ce travail de recherche contribue à l’optimisation d’un problème de tournées de service à domicile multi-dépôt, multi-période avec fenêtres de temps de vie réelle. Le problème vient d’un contexte réaliste et est formulé comme un modèle en Mixed Integer Programming (MIP). Les résultats avec Cplex montrent que ce problème ne peut être résolu par des méthodes exactes dans un délai raisonnable pour une utilisation pratique. Par conséquent, nous introduisons des heuristiques. Premièrement, les heuristiques de recherche locales sont utilisées pour résoudre le problème. Les solutions réalisables initiales sont générées par une heuristique de construction et plusieurs heuristiques de recherche locales sont appliquées pour obtenir des solutions dans un temps de calcul assez court. Ensuite, nous proposons un algorithme génétique avec une nouvelle représentation du chromosome et de nouveaux opérateurs génétiques pour le problème abordé. Enfin, nous considérons un algorithme génétique avec contrôle de la diversité pour problèmes à grande échelle. Les solutions infaisables sont prises en compte dans la population et la contribution à la diversité fait partie de l’évaluation afin d’éviter une recherche prématurée. Ces méthodes ont été mises en œuvre avec succès pour optimiser le problème de routage. / The logistics performance of enterprises and the optimization of transportation have become a great issue in recent years. Field force planning and optimization is a new challenge for the service sector. In order to increase productivity and reduce cost of logistics, this research contributes to the optimization of a real-life multi-depot multi-period field service routing problem with time window. The problem is abstracted from the realistic problem and formulated as a Mixed Integer Programming (MIP) model. Computational results with Cplex show that this problem cannot be solved by exact methods in reasonable time for practical use. First, local search heuristics are used for solving the problem. Initial feasible solutions are generated by a constructive heuristic and several local search heuristics are applied to obtain solutions in a very short computing time. Then we propose a genetic algorithm with new representation of chromosome and new genetic operators for the addressed problem. Finally we consider a genetic algorithm with diversity control to deal with large scale problems. Infeasible solutions are taken account in the population and the diversity contribution is part of the evaluation to avoid premature of search. These methods have been successfully implemented to the optimization of the routing problem

Satellite power systems can be understood as islanded dc microgrids supplied by specialized and coordinated solar cell arrays augmented by electrochemical battery systems to handle high-power loads and periods of eclipse. The periodic availability of power, the limited capacity of batteries, and the dependence of all mission service on power consumption create a unique situation in which temporal power and energy scarcity exist. A multi-period model of an orbital satellite power system’s performance over a mission’s duration can be constructed. A modular power system architecture is used to characterize the system’s constraints. Using mathematical programming, an optimization problem can be posed such that the optimal power and energy ratings for the power system are determined for any load schedule imposed by a given mission’s requirements. The optimal energy trajectory of the electrical power system over a mission’s duration is also determined when the mathematical programming problem is solved. A generic set of mission requirements is identified to test this approach, but the objective function of the resulting optimization problem can be modified to return different results. These results can provide a clear illustration of the trade-offs that designers of such power systems consider in the design process.

Neste trabalho, deriva-se uma política de escolha ótima baseada na análise de média-variância para o Erro de Rastreamento no cenário Multi-período - ERM -. Referindo-se ao ERM como a diferença entre o capital acumulado pela carteira escolhida e o acumulado pela carteira de um benchmark. Assim, foi aplicada a metodologia abordada por Li-Ng em [24] para a solução analítica, obtendo-se dessa maneira uma generalização do caso uniperíodo introduzido por Roll em [38]. Em seguida, selecionou-se um portfólio do mercado de ações brasileiro baseado no fator de orrelação, e adotou-se como benchmark o índice da bolsa de valores do estado de São Paulo IBOVESPA, além da taxa básica de juros SELIC como ativo de renda fixa. Dois casos foram abordados: carteira composta somente de ativos de risco, caso I, e carteira com um ativo sem risco indexado à SELIC - e ativos do caso I (caso II). / In this work, an optimal policy for portfolio selection based on mean-varian e analysis for the multi-period tracking error - ERM - was derived. ERM is understood as the difference between the capital raised by the selected portfolio and benchmark portfolio. Thus, the methodology discussed by Li-Ng in [24] for analytical solution was applied, generalizing the single period case introduced by Roll in [38]. Then, it was selected a portfolio from the Brazilian stock trading based on the correlation factor, and adopted as benchmark the index of the stock trading of São Paulo State IBOVESPA, and the basic interest rate SELIC as fixed income asset. Two cases were dealt: portfolio composed of risky assets only, case I, and portfolio with a risk-free asset - indexed to SELIC - and assets of the case I (case II).

Neste trabalho, estudamos o problema de corte bidimensional multiperíodo com sobras aproveitáveis, que consiste em cortar objetos grandes visando a produção de um conjunto de itens menores. Supomos um horizonte de planejamento finito com uma quantidade finita de períodos entre os tempos inicial e final. Primeiramente consideramos uma versão determinística em que conhecemos, à priori, os itens solicitados em uma ordem de trabalho e o custo dos objetos a cada período. Algumas das sobras geradas durante o processo de corte dos itens solicitados em um período podem ser utilizadas como objetos no futuro. As sobras que podem ser usadas no futuro são denominadas sobras aproveitáveis. De forma geral, uma sobra é considerada aproveitável se possui dimensões iguais ou superiores as de algum item de uma lista pré-definida para o período. O objetivo é minimizar o custo total dos objetos utilizados para satisfazer a ordem de trabalho dos itens solicitados de todo o horizonte considerado. Havendo soluções com o mesmo custo, desejamos encontrar aquela que, no fim do horizonte de tempo considerado, maximize o valor das sobras aproveitáveis remanescentes. Apresentamos uma modelagem matemática do problema usando uma formulação em dois níveis, que é transformada em um modelo de programação linear inteira mista, devido às características do problema. Considerando a dificuldade em resolver o modelo desenvolvido, apresentamos uma proposta de uma abordagem heurística baseada em Programação Dinâmica Aproximada (PDA) para lidar com o problema proposto. Outras opções baseadas em estratégias do tipo horizonte rolante e relax-and-fix também são consideradas. Consideramos também o cenário onde não conhecemos de antemão os itens da ordem de trabalho e o custo dos objetos, mas temos informações das distribuições de probabilidade de ambos. Nesse caso, apresentamos uma abordagem baseada em programação dinâmica aproximada para estimar a melhor estratégia a ser seguida em cada período. Comparamos os resultados obtidos pela PDA com os resultados encontrados por um método guloso. Em cenários adequados, os resultados mostram que a PDA consegue soluções superiores ao método guloso. / In this research, we study the multi-period two-dimensional cutting problem with usable leftover, which consists of cutting objects to produce a set of items. We assume a finite planning horizon with a finite amount of periods between the initial and final times. First we consider a deterministic version in which we know, a priori, the set of ordered items and the cost of the objects at each period. Some of the leftovers generated during the cutting process of the ordered items in a period may be used as objects in the future. The leftovers that can be used in the future are called usable leftovers. In general, a leftover is considered usable if it has dimensions equal to or greater than that of some item from a predefined list for the period. The goal is to minimize the total cost of the objects used to cut the set of ordered items of the entire considered horizon. If there are solutions with the same cost, we wish to find one that, at the end of the considered time horizon, maximizes the value of the remaining usable leftovers. We present a mathematical model of the problem using a bilevel formulation, which is transformed into a mixed integer linear programming model, due to the characteristics of the problem. Considering the difficulty in solving the developed model, we propose a heuristic approach based on approximate dynamic programming (ADP) to deal with the proposed problem. Other options based on the rolling horizon and relax-and-fix strategies are also considered. We also consider the scenario where we do not know in advance the set of ordered items and the cost of the objects, but we have information about the probability distributions of both. In this case, we present an approach based on approximate dynamic programming to estimate the best strategy to be followed at each period. We compared the results obtained by the ADP with the results found by a greedy method. In suitable scenarios, the results show that the ADP achieves superior solutions to the greedy method.

The main objective of this thesis is the valuation of distribution rights of the company TROJA MOTO s.r.o.. Derivative goal is the specification of an intangible asset (distribution rights), including a comparison of benefits and costs that result from the usage of this right for the company. For the valuation of distribution rights, I apply the valuation method of multi-period excess-earnings, which is suitable for the valuation of specific intangibles. The main part are the strategic and financial analysis, which are important basis for the financial plan. The final chapter includes the calculated value of distribution rights themselves.

This diploma thesis engages in the growing importance of the industrial property in the present world. The aim of my work is valuation of a trademark as an intangible property of a firm. This work is divided into two parts. The first part of thesis describes term of trademark and brand, partition of intangible property in czech legislation. Follows describtion procedure of the specific trademark valuation. The second part of thesis describes the procedure of the specific trademark valuation using cost-method, relief from royalty method and multi-period excess earnings method. In conclusion of the thesis there are evaluations of using methods and procedures.