A competição no mercado globalizado, do ponto de vista do consumidor, aumentou consideravelmente a oferta de produtos e serviços, permitindo a escolha pela qualidade, preço, prazos e/ou disponibilidade. Entretanto, do ponto de vista das empresas, o desafio de atuar em um mercado saturado, dinâmico, competitivo e com aumento da demanda de produção orientado ao consumidor altera consideravelmente toda a estrutura da empresa. Desta forma, a flexibilidade torna-se um pré-requisito fundamental para que as empresas possam atuar neste mercado. Considerando-se o contexto apresentado, o objeto de estudo do presente trabalho são os Sistemas Produtivos Flexíveis (SPFs). Os SPFs são sistemas concebidos para atender às necessidades de um mercado dinâmico e competitivo. Isto causa complexidade no comportamento global desses sistemas exigindo diferentes propostas para o projeto de Sistemas de Controle de SPFs. A complexidade advém do fato de se perder a informação quanto ao pré-determinismo da seqüência de eventos que ocorrem no sistema global, além do fato de não existir previamente a definição de todos os processos de transporte com a designação prévia de todas as ordens de transporte que os transportadores presentes na planta devem executar. Neste contexto, o objetivo deste trabalho é apresentar como resultado uma proposta de sistema de controle modular para SPFs que atenda os requisitos de fluxo de informações envolvendo hierarquia e colaboração simultânea entre os módulos, respeitando a diversidade semântica presente na arquitetura. Apresenta-se então uma sistematização de projeto dos diversos módulos de controle e uma proposta de implementação de um algoritmo de designação dos transportadores para ser utilizado em tempo real permitindo a realização eficiente das atividades de transporte. / The competition in the globalized market increased considerably the demand for products and services to the customer point of view. However, the consumer\'s driven demand change the whole structure of the company. Thus, flexibility becomes an essential pre-requisite for companies to compete in the market. Considering the context presented, the object of study of this work is the Flexible Production System (FPS). The FPS must fulfill the needs on a dynamic and competitive market. This situation causes complexity in the overall performance of these systems, requiring different proposals for the design of the control systems. The complexity arises due to the fact that loses the information about the sequence of events that occur in the global system. Besides there is no previous exist definition for all processes of transport and prior designation for it. In this context, the objective of this work is to present a proposal for modular control systems to FPS that fits the requirements of information flow involving hierarchy and simultaneous collaboration between the modules. It will be presented systematization for the modular design of control as well as an implementation of an algorithm for designation of transport in real time leading to an efficient management of these activities.

Em um ambiente altamente competitivo, onde a disputa por clientes rentáveis torna-se cada vez mais acirrada, a crescente importância das ações de marketing como atividades estratégicas ganham destaque. Este papel traz consigo cobranças, deveres e responsabilidades a serem cumpridas em um ambiente corporativo. Torna-se natural que a alta gestão das companhias exija avaliações de desempenho pautadas pela mensuração dos resultados através de métricas bem definidas e estabelecidas. Mais que indicadores de resultados, as corporações necessitam justificar seus investimentos com maior responsabilidade de forma a garantir transparência junto aos Stakeholders, assim como proporcionar uma melhor remuneração aos Shareholders. Os princípios de Governança Corporativa, Accountability e Compliance tornam-se essenciais nas corporações que exercem as melhores práticas de mercado e utilizam métricas de desempenho como meio de prestação de contas. Àquelas que possuem orientação para mercado necessitam de métricas adequadas e específicas que meçam a satisfação do cliente visando assegurar-lhes uma entrega de valor superior frente aos competidores, pois clientes são ativos de marketing significativos e geram valor para o acionista. Esta dissertação tem por objetivo estudar métricas de desempenho, que são utilizadas regularmente pelos gestores de marketing para análise de resultados, alocação de recursos e Accountability. Para que o objetivo ora proposto fosse alcançado, o estudo foi organizado em duas etapas, onde a primeira aborda o levantamento do referencial teórico, que visou organizar o conhecimento das métricas de desempenho proposto em literatura, sobre a evolução do pensamento de marketing e sua influência no desenvolvimento e utilização de métricas para aferir resultados, alocação de recursos, valor para o Acionista, Accountability em marketing e seus objetivos, orientação para mercado e Métricas de desempenho e aplicações. A segunda etapa abrange a pesquisa empírica, que proporcionou a identificação das métricas mais difundidas e utilizadas pelas empresas farmacêuticas de medicamentos isentos de prescrição no Brasil e seus gestores de marketing, para aferir resultados, subsidiar alocação de recursos e Accountability em Marketing, bem como àquelas que são consideradas mais importantes por estes profissionais. Este estudo foi desenvolvido por meio de uma abordagem quantitativa, através de levantamento, utilizando um questionário estruturado (survey), via internet, o qual foi aplicado em gestores de marketing de empresas farmacêuticas de medicamentos isentos de prescrição. A análise dos dados obtidos por meio da pesquisa empírica foi descrita de forma detalhada, assim como os achados da utilização das métricas em acordo com perfil da empresa, seja por tamanho ou origem. Dentre os principais resultados encontrados e por meio da análise das evidencias apresentadas, as métricas mais utilizadas são as métricas de margens e lucros e financeiras, quando analisamos empresas multinacionais, e, as empresas nacionais, embora também façam uso das métricas de margens e lucros, utilizam com mais intensidade em seu lugar as métricas de mapeamento, segmentação e posicionamento de mercado, associadas às financeiras. / In a highly competitive environment where competition for profitable customers becomes increasingly fierce, the growing importance of marketing actions as strategic activities is highlighted. This importance carries duties, responsibilities, and requests to be met in a corporate environment. It becomes natural that top management of companies requires performance evaluations based on well-defined and established measurements of results. More than indicators of results, corporations need to justify their investments with greater responsibility to ensure transparency with stakeholders, as well as provide better remuneration to Shareholders. Principles of Corporate Governance, Accountability and Compliance become essential in corporations that exert the best market practices and use performance metrics as a means of accountability. Those that have market orientation require appropriate and specific metrics that measure customer satisfaction in order to ensure the delivery of superior value compared to competitors, because customers are marketing assets and generate significant shareholder value. This thesis aims to study performance metrics, which are regularly used by marketing managers to analyze results, resource allocation and Accountability. To reach the proposed objective, this study was organized in two parts: the first deals with the survey of theoretical framework, which aimed at organizing knowledge of the performance metrics proposed in the literature, the evolution of marketing ideas and their influence in the development and use of metrics to measure results, resource allocation, value for Shareholders, Accountability and marketing objectives, market orientation and performance metrics and applications. The second part covers the empirical research, which yielded the identification of the most widespread marketing metrics used by non-prescription drug pharmaceutical companies in Brazil and their managers to measure outcomes, support resource allocation and Accountability in Marketing, as well as those that are considered most important by these professionals. This study was conducted via a quantitative approach, through a structured questionnaire (survey), via the Internet, where the survey instrument was proposed to marketing managers of non-prescription drug pharmaceutical companies. The analysis of data obtained through empirical research has been described in detail, as well as the findings of the use of metrics in accordance with company profile, either by size or origin. Among the main findings and analyzing the evidence presented, the most frequently used metrics are financial and margins & profits when we are analyzing multinational companies. When we analyze the domestic companies, although it also makes use of margins & profits metrics, they used more intensively in its place mapping, segmentation and market positioning metrics, associated with financial metrics.

The price of computers has dropped drastically over the past years enabling many households to have at least one computer. At the same time, the performance of computers has skyrocketed, far surpassing what a typical user needs, and most of the computational power of personal computers is wasted. Volunteer computing projects attempt to use this wasted computational power in order to solve problems that would otherwise be computationally infeasible. Some of these problems include medical applications like searching for cures for AIDS and cancer. However, the number of volunteer computing projects is increasing rapidly, requiring improvements in the field of volunteer computing to enable the increasing number of volunteer projects to continue making significant progress. This dissertation examines two ways to increase the productivity of volunteer computing: using the volunteered CPU cycles more effectively and exploring ways to increase the amount of CPU cycles that are donated. Each of the existing volunteer computing projects uses one of two task retrieval policies to enable the volunteered computers participating in projects to retrieve work. This dissertation compares the amount of work completed by the volunteered computers participating in projects based on which of the two task retrieval techniques the project employs. Additional task retrieval policies are also proposed and evaluated. The most commonly used task retrieval policy is shown to be less effective than both the less frequently used policy and a proposed policy. The potential that video game consoles have to be used for volunteer computing is explored, as well as the potential benefits of constructing different types of volunteer computing clients, rather than the most popular client implementation: the screensaver. In addition to examining methods of increasing the productivity of volunteer computing, 140 traces of computer usage detailing when computers are available to participate in volunteer computing is collected and made publicly available. Volunteer computing project-specific information that can be used in researching how to improve volunteer computing is collected and combined into the first summary of which we are aware.

"Resource sharing between applications can significantly improve the resources required for all, which can reduce cost, and improve performance. Isolating resources on the other hand can also be beneficial as the failure or significant load on one application does not affect another. There is a delicate balance between resource sharing and resource isolation. Virtual machines may be a solution to this problem with the added benefit of being able to perform more dynamic load balancing, but this solution may be at a significant cost in performance. This thesis compares three different configurations for machines running application servers. It looks at speed at which a new application server can be started up, resource sharing and resource isolation between applications in an attempt to quantify the tradeoffs for each type of configuration."

There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for resources management in diverse wireless networks. The core operation of the proposed architecture is decision-making for resource allocation and system’s parameters adaptation. Thus, we develop the decision-making mechanism using different artificial intelligence techniques, evaluate the performance achieved and determine the tradeoff of using one technique over the others. The techniques include decision-trees, genetic algorithm, hybrid engine based on decision-trees and case based reasoning, and supervised engine with machine learning contribution to determine the ultimate technique that suits the current environment conditions. All the proposed techniques are evaluated using testbed implementation in different topologies and scenarios. LTE networks have been considered as a potential environment for demonstration of our proposed cognitive based resource allocation techniques as they lack of radio resource management. In addition, we explore the use of enhanced online learning to perform efficient resource allocation in the upcoming 5G networks to maximize energy efficiency and data rate. The considered 5G structures are heterogeneous multi-tier networks with device to device communication and heterogeneous cloud radio access networks. We propose power and resource blocks allocation schemes to maximize energy efficiency and data rate in heterogeneous 5G networks. Moreover, traffic offloading from large cells to small cells in 5G heterogeneous networks is investigated and an online learning based traffic offloading strategy is developed to enhance energy efficiency. Energy efficiency problem in heterogeneous cloud radio access networks is tackled using online learning in centralized and distributed fashions. The proposed online learning comprises improvement features that reduce the algorithms complexities and enhance the performance achieved.

Developments such as machine-to-machine communications and multimedia services are placing growing demands on high-speed reliable transmissions and limited wireless spectrum resources. Although multiple-input multiple-output (MIMO) systems have shown the ability to provide reliable transmissions in fading channels, it is not practical for single-antenna devices to support MIMO system due to cost and hardware limitations. Cooperative communication allows single-antenna devices to share their spectrum resources and form a virtual MIMO system where their quality of service (QoS) may be improved via cooperation. Most cooperative communication solutions are based on fixed spectrum access schemes and thus cannot further improve spectrum efficiency. In order to support more users in the existing spectrum, we consider dynamic spectrum access schemes and cognitive radio techniques in this dissertation. Our work includes the modelling, characterization and optimization of QoS-constrained cooperative networks and cognitive radio networks. QoS constraints such as delay and data rate are modelled. To solve power control and channel resource allocation problems, dynamic power control, matching theory and multi-armed bandit algorithms are employed in our investigations. In this dissertation, we first consider a cluster-based cooperative wireless network utilizing a centralized cooperation model. The dynamic power control and optimization problem is analyzed in this scenario. We then consider a cooperative cognitive radio network utilizing an opportunistic spectrum access model. Distributed spectrum access algorithms are proposed to help secondary users utilize vacant channels of primary users in order to optimize the total utility of the network. Finally, a noncooperative cognitive radio network utilizing the opportunistic spectrum access model is analyzed. In this model, primary users do not communicate with secondary users. Therefore, secondary users are required to find vacant channels on which to transmit. Multi-armed bandit algorithms are proposed to help secondary users predict the availability of licensed channels. In summary, in this dissertation we consider both cooperative communication networks and cognitive radio networks with QoS constraints. Efficient power control and channel resource allocation schemes have been proposed for optimization problems in different scenarios.

Device-to-device (D2D) communications underlaying a cellular infrastructure takes advantage of the physical proximity of communicating devices and increasing resource utilisation. However, adopting D2D communications in complex scenarios poses substantial challenges for the resource allocation design. Meanwhile, matching theory has emerged as a promising framework for wireless resource allocation which can overcome some limitations of game theory and optimisation. This thesis focuses on the resource allocation optimisation for D2D communications based on matching theory. First, resource allocation policy is designed for D2D communications underlaying cellular networks. A novel spectrum allocation algorithm based on many-to-many matching is proposed to improve system sum rate. Additionally, considering the quality-of-service (QoS) requirements and priorities of di erent applications, a context-aware resource allocation algorithm based on many-to-one matching is proposed, which is capable of providing remarkable performance enhancement in terms of improved data rate, decreased packet error rate (PER) and reduced delay. Second, to improve resource utilisation, joint subchannel and power allocation problem for D2D communications with non-orthogonal multiple access (NOMA) is studied. For the subchannel allocation, a novel algorithm based on the many-to-one matching is proposed for obtaining a suboptimal solution. Since the power allocation problem is non-convex, sequential convex programming is adopted to transform the original power allocation problem to a convex one. The proposed algorithm is shown to enhance the network sum rate and number of accessed users. Third, driven by the trend of heterogeneity of cells, the resource allocation problem for NOMA-enhanced D2D communications in heterogeneous networks (HetNets) is investigated. In such a scenario, the proposed resource allocation algorithm is able to closely approach the optimal solution within a limited number of iterations and achieves higher sum rate compared to traditional HetNets schemes. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and performance of proposed algorithm is evaluated via comprehensive simulations. This thesis concludes that matching theory based resource allocation for D2D communications achieves near-optimal performance with acceptable complexity. In addition, the application of D2D communications in NOMA and HetNets can improve system performance in terms of sum rate and users connectivity.

Até 2020 espera-se que as redes celulares aumentam em dez vezes a área de cobertura, suporte cem vezes mais equipamentos de usuários e eleve a capacidade da taxa de dados em mil vezes, comparada as redes celulares atuais. A densa implantação de pequenas células é considerada uma solução promissora para alcançar essas melhorias, uma vez que aproximar as antenas dos usuários proporciona maiores taxas de dados, devido à qualidade do sinal em curtas distâncias. No entanto, operar um grande número de antenas pode aumentar significativamente o consumo de energia da infraestrutura de rede. Além disso, a grande inserção de novos rádios pode ocasionar maior interferência espectral entre as células. Nesse cenário, a gestão dos recursos de rádio é essencial devido ao impacto na qualidade do serviço prestado aos usuários. Por exemplo, baixas potências de transmissão podem deixar usuários sem conexão, enquanto altas potências elevam a possibilidade de ocorrência de interferência. Além disso, a reutilização não planejada dos recursos de rádio causa a ocorrência de interferência, resultando em baixa capacidade de transmissão, enquanto a subutilização de recursos limita a capacidade total de transmissão de dados. Uma solução para controlar a potência de transmissão, atribuir os recursos de rádio e garantir o serviço aos usuários é essencial. Nesta dissertação, é proposto um algoritmo adaptativo de Monte Carlo para realizar alocação global de recursos de forma eficiente em termos de energia, para arquiteturas Heterogeneous Cloud Radio Access Network (H-CRAN), projetadas como futuras redes de quinta geração (5G). Uma solução eficiente para a alocação de recursos em cenários de alta e baixa densidade é proposta. Nossas contribuições são triplas: (i) proposta de uma abordagem global para o problema de atribuição de recursos de rádio na arquitetura HCRAN, cujo caráter estocástico garante uma amostragem geral de espaço de solução; (ii) uma comparação crítica entre nossa solução global e um modelo local; (iii) a demonstração de que, para cenários de alta densidade, a Eficiência Energética não é uma medida adequada para alocação eficiente, considerando a capacidade de transmissão, justiça e total de usuários atendidos. Além disso, a proposta é comparada em relação a três algoritmos de alocação de recursos de última geração para redes 5G. / Up until 2020 it is expected that cellular networks must raise the coverage area in 10-fold, support a 100-fold more user equipments, and increase the data rate capacity by a 1000-fold in comparison with current cellular networks. The dense deployment of small cells is considered a promising solution to reach such aggressive improvements, once it moves the antennas closer to the users, achieving higher data rates due to the signal quality at short distances. However, operating a massive number of antennas can significantly increase the energy consumption of the network infrastructure. Furthermore, the large insertion of new radios brings greater spectral interference between the cells. In this scenery, the optimal management of radio resources turn an exaction due to the impact on the quality of service provided to the users. For example, low transmission powers can leave users without connection, while high transmission powers can contribute to inter radios interference. Furthermore, the interference can be raised on the unplanned reuse of the radio resources, resulting in low data transmission per radio resource, as the under-reuse of radio resources limits the overall data transmission capacity. A solution to control the transmission power, assign the spectral radio resources, and ensure the service to the users is essential. In this thesis, we propose an Adaptive Monte Carlo algorithm to perform global energy efficient resource allocation for Heterogeneous Cloud Radio Access Network (HCRAN) architectures, which are forecast as future fifth-generation (5G) networks. We argue that our global proposal offers an efficient solution to the resource allocation for both high and low density scenarios. Our contributions are threefold: (i) the proposal of a global approach to the radio resource assignment problem in H-CRAN architecture, whose stochastic character ensures an overall solution space sampling; (ii) a critical comparison between our global solution and a local model; (iii) the demonstration that, for high density scenarios, Energy Efficiency is not a well suited metric for efficient allocation, considering data rate capacity, fairness, and served users. Moreover, we compare our proposal against three state-of-the-art resource allocation algorithms for 5G networks.

Limited wireless resources, such as spectrum and maximum power, give rise to various resource allocation problems that are interesting both from theoretical and application viewpoints. While the problems in some of the wireless networking applications are amenable to general resource allocation methods, others require a more specialized approach suited to their unique structural characteristics. We study both types of the problems in this thesis. We start with a general problem of alpha-fair packing, namely, the problem of maximizing sum_j {w_j f_α(x_j)}, where w_j > 0, ∀j, and (i) f_α(x_j)=ln(x_j), if α = 1, (ii) f_α(x_j)= {x_j^(1-α)}/{1-α}, if α ≠ 1,α > 0, subject to positive linear constraints of the form Ax ≤ b, x ≥ 0, where A and b are non-negative. This problem has broad applications within and outside wireless networking. We present a distributed algorithm for general alpha that converges to an epsilon-approximate solution in time (number of distributed iterations) that has an inverse polynomial dependence on the approximation parameter epsilon and poly-logarithmic dependence on the problem size. This is the first distributed algorithm for weighted alpha-fair packing with poly-logarithmic convergence in the input size. We also obtain structural results that characterize alpha-fair allocations as the value of alpha is varied. These results deepen our understanding of fairness guarantees in alpha-fair packing allocations, and also provide insights into the behavior of alpha-fair allocations in the asymptotic cases when alpha tends to zero, one, and infinity. With these general tools on hand, we consider an application in wireless networks where fairness is of paramount importance: rate allocation and routing in energy-harvesting networks. We discuss the importance of fairness in such networks and cases where our results on alpha-fair packing apply. We then turn our focus to rate allocation in energy harvesting networks with highly variable energy sources and that are used for applications such as monitoring and tracking. In such networks, it is essential to guarantee fairness over both the network nodes and the time slots and to be as fair as possible -- in particular, to require max-min fairness. We first develop an algorithm that obtains a max-min fair rate assignment for any routing that is specified at the input. Then, we consider the problem of determining a "good'' routing. We consider various routing types and either provide polynomial-time algorithms for finding such routings or prove that the problems are NP-hard. Our results reveal an interesting trade-off between the complexities of computation and implementation. The results can also be applied to other related fairness problems. The second part of the thesis is devoted to the study of resource allocation problems that require a specialized approach. The problems we focus on arise in wireless networks employing full-duplex communication -- the simultaneous transmission and reception on the same frequency channel. Our primary goal is to understand the benefits and complexities tied to using this novel wireless technology through the study of resource (power, time, and channel) allocation problems. Towards that goal, we introduce a new realistic model of a compact (e.g., smartphone) full-duplex receiver and demonstrate its accuracy via measurements. First, we focus on the resource allocation problems with the objective of maximizing the sum of uplink and downlink rates, possibly over multiple orthogonal channels. For the single-channel case, we quantify the rate improvement as a function of the remaining self-interference and signal-to-noise ratios and provide structural results that characterize the sum of uplink and downlink rates on a full-duplex channel. Building on these results, we consider the multi-channel case and develop a polynomial time algorithm which is nearly optimal in practice under very mild restrictions. To reduce the running time, we develop an efficient nearly-optimal algorithm under the high SINR approximation. Then, we study the achievable capacity regions of full-duplex links in the single- and multi-channel cases. We present analytical results that characterize the uplink and downlink capacity region and efficient algorithms for computing rate pairs at the region's boundary. We also provide near-optimal and heuristic algorithms that "convexify'' the capacity region when it is not convex. The convexified region corresponds to a combination of a few full-duplex rates (i.e., to time sharing between different operation modes). The analytical results provide insights into the properties of the full-duplex capacity region and are essential for future development of fair resource allocation and scheduling algorithms in Wi-Fi and cellular networks incorporating full-duplex.

Este trabalho apresenta um modelo com firmas heterogêneas e aprendizagem. As predições desse modelo mostram que, sob certas condições, as políticas de desenvolvimento podem gerar aumento de produtividade no curto prazo, masmisallocation e perda de produtividade agregada no longo prazo. Isso ocorre caso um componente de produtividade de longo prazo seja imperfeitamente observável no curto prazo devido a choques temporários, e o capital seja especifico e irreversível em alguns setores. Os resultados mostram que o ideal seria aprender sobre componentes de longo prazo da produtividade antes de investir. No entanto, acelerar investimentos nos setores de maior produtividade gera ganhos de produtividade no curto prazo, uma vez que maior produtividade no curto prazo está correlacionada com maior produtividade no longo prazo. Como será discutido, este fato poderia motivar governos à incentivar setores de alta produtividade observada, ainda que estes incentivos fossem socialmente subótimos. / This project aims to present a model that shows that, in a single economic environment, government stimulus to sectors, which features high productivity, can generate short-term productivity gains aggregates, but misallocation and long-term aggregate productivity loss. This may occur if a long-term productivity component is imperfectly observable in the short term due to temporary shocks, and capital is specific and irreversible in some sectors. The ideal be learn about long-term components of the productivity before investing. However accelerate investments in the sectors of higher productivity generates productivity gains in the short term, since higher productivity in the short term this correlated with higher productivity in the long run. As discussed, this fact would motivate governments to encourage high productivity observed sectors, although these incentives were socially suboptimal.