In this study, we have aimed to optimize patient flow in emergency departments while minimizing associated costs. In order to be able to compare the effect of any changes, we developed a simulation model for an emergency department using Queuing theory, and regarding the optimization we utilized Genetic Algorithm to find the best change. Basically, we have designed a Discrete Event based, multi-class, multi-server queuing network as we have considered the emergency department a set of stages associated with a queue of patients waiting to be served. Each stage has multiple service providers such as Nurses, Doctors or other staff. We also classified patients passing through the stages, according to their acuity level and personal characteristics. Then, we defined a function as a measure of the ED performance in respect to the calculated wait times and the cost. Finally, we developed a customized genetic algorithm to find the best performance which reflects the best allocation of service providers into multiple stages of the emergency department.

In this work, we produce several prediction models for aspects of hospital emergency departments. Firstly, we demonstrate the use of a recurrent neural network to predict the rate of patient arrival at a hospital emergency department. The prediction is made on a per hour basis using date, time, calendar, and weather information. Then, we present our comparison of two prediction systems on the task of replicating the human decisions of patient admittance in a typical American emergency department. Again, a recurrent neural network (RNN) was trained to learn the task of selecting the next patient from the waiting room/queue to be admitted for treatment. Lastly, we present our attempt to produce a regression model that can predict the likelihood that a given patient will leave after waiting a specific amount of time in the emergency department’s waiting-room/queue. Such a model could be used to optimize the patient’s waiting-room/queue of an ED to minimize the likelihood of patients leaving without receiving care.

This thesis describes the refinement of an Architecture Flow Optimization (AFO) tool for naval surface ship design, specifically focusing on the development of new network and matrix-based methods for AFO formulation and their application in Concept Development. The AFO tool analyzes and optimizes the flow of energy through the ship's Vital Components (VCs) interfacing with a Ship Synthesis and Product Model (SSM), ensuring that all physical and operational constraints are satisfied while minimizing system cost across multiple intact and damaged operational scenarios. The total ship system is described by physical and logical architectures in a network structure comprised of vital component nodes and arcs. These elements form the basis of a linear system of equations in matrix form, the manipulation of which relies heavily on linear algebra and matrix operations. The matrix system of equations is solved using linear programming with a significant improvement in computational efficiency. The solution supports the sizing of individual vital components and the refinement of system logical architecture. It also provides the basic AFO engine necessary to support future refinement of a dynamic architecture flow optimization (DAFO) with the computational speed necessary for rapid solution of dynamic mission scenarios insuring optimized and feasible warfighting reconfiguration, with and without damage. / Master of Science / This thesis describes the refinement of an Architecture Flow Optimization (AFO) tool for naval surface ship design, specifically focusing on the development of new network and matrix-based methods for AFO formulation and their application in naval ship Concept Development processes. The Architecture Flow Optimization tool analyzes and optimizes the flow of energy through the ship's Vital Components (VCs). The AFO tool completes this task by interfacing with a Ship Synthesis and Product Model (SSM), ensuring that all of the ship's physical and operational constraints are satisfied. This is done while minimizing the ship system cost across multiple intact and damaged operational scenarios. The total ship system is described by physical and logical architectures in a network structure comprised of vital components (nodes) and their connections (arcs). These elements form the basis of a linear system of equations in matrix form, the manipulation of which relies heavily on linear algebra and matrix operations. The matrix system of equations is solved using a linear programming algorithm with a significant improvement in computational speed. The solution provided from the optimization supports the sizing of individual vital components and the refinement of the ship system logical architecture. It also provides the basic AFO engine necessary to support future refinement of a dynamic architecture flow optimization (DAFO) with the computational speed necessary for rapid solution of dynamic mission scenarios insuring optimized and feasible warfighting reconfiguration, with and without damage.

This thesis discusses the development of a naval ship distributed system architectural framework and related design tools that can be used during ship Concept and Requirements Exploration (CandRE). This architectural framework includes architectures for ship operations, the physical arrangement of Mission Power and Energy Systems (MPES) vital components within the ship, the logical relationship between MPES vital components, and simple energy and data models of MPES functions. This architectural framework is implemented through integrated Ship Behavior Interaction Models (SBIMs) that include the following: Warfighting Model (WM), Ship Operational Model (OM), Capability Model (CM), and Dynamic Architecture Flow Optimization (DAFO). These models provide a critical interface between logical and operational architectures, quantifying warfighting capabilities through system measures of performance at specific capability nodes. These models' interface with each other in the warfighting environment to guide the alignment of MPES vital systems using a DAFO. The integrated models quantify the performance of tasks enabled by capabilities through system measures of performance at specific capability nodes, enabling the simulation of the MPES configuration in operational situations. / Master of Science / This thesis discusses the development of a naval ship distributed system architectural framework and related design tools that can be used during ship Concept and Requirements Exploration (CandRE). This architectural framework includes architectures for ship operations, the physical arrangement of Mission Power and Energy Systems (MPES) within the ship, the logical relationship between MPES, and simple energy and data models of MPES. This architectural framework is implemented through integrated Ship Behavior Interaction Models (SBIMs) that include the following: Warfighting Model (WM), Ship Operational Model (OM), Capability Model (CM), and Dynamic Architecture Flow Optimization (DAFO). These models provide a critical interface between logical and operational architectures, quantifying warfighting capabilities through system measures of performance. These models' interface with each other in the warfighting environment to guide the alignment of MPES during operations. The integrated models quantify the performance of tasks enabled by capabilities through system measures of performance at specific capability nodes, enabling the simulation of the MPES configuration in operational situations.

In the last two-decades data center networks have evolved to become a key element in improving levels of productivity and competitiveness for different types of organizations. Traditionally data center networks have been constructed with 3 layers of switches, Edge, Aggregation, and Core. Although this Three-Tier architecture has worked well in the past, it poses a number of challenges for current and future data centers. Data centers today have evolved to support dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. Also The emergence of "Big Data" applications that exchange large volumes of information have created large persistent flows that need to coexist with other traffic flows. The Three-Tier architecture and current routing schemes are no longer sufficient for achieving high bandwidth utilization. Data center networks should be built in a way where they can adequately support virtualization and cloud computing technologies. Data center networks should provide services such as, simplified provisioning, workload mobility, dynamic routing and load balancing, equidistant bandwidth and latency. As data center networks have evolved the Three-Tier architecture has proven to be a challenge not only in terms of complexity and cost, but it also falls short of supporting many new data center applications. In this work we propose OneSwitch: A switch architecture for the data center. OneSwitch is backward compatible with current Ethernet standards and uses an OpenFlow central controller, a Location Database, a DHCP Server, and a Routing Service to build an Ethernet fabric that appears as one switch to end devices. This allows the data center to use switches in scale-out topologies to support hosts in a plug and play manner as well as provide much needed services such as dynamic load balancing, intelligent routing, seamless mobility, equidistant bandwidth and latency. / PHD

Orientador: Rubén Augusto Romero Lazaro / Resumo: O problema de reconfiguração de sistemas de distribuição de energia elétrica consiste em encontrar uma configuração radial por meio da permutação do estado das chaves (abertura ou fechamento) dos ramos de um sistema elétrico. O objetivo é de se alcançar a minimização das perdas elétricas. Cada configuração radial só é considerada factível se respeitar certas restrições operacionais como o limite de tensão nas barras e os limites de correntes nos circuitos. O modelo tratado neste trabalho apresenta explosão combinatória e difícil tratabilidade por meio de métodos convencionais de otimização. O problema, computacionalmente falando, é considerado Não-Polinomial Completo (NPC), pois não possui uma resposta em tempo polinomial a partir de uma entrada definida. Neste trabalho são apresentadas três técnicas meta-heurísticas para se tratar o problema de reconfiguração de sistemas de distribuição de energia elétrica, totalmente diferentes entre uma e outra, atuando em conjunto, para somente um nível de demanda, no intuito de se encontrar a topologia ótima, com o objetivo de se minimizar as perdas elétricas ativas. Além disso, propôs-se modificar o paradigma clássico de implementação estático deste tipo de problema para o paradigma de programação dinâmica por meio de árvores com filhos variados a fim de que a estrutura de dados utilizada representasse fielmente um sistema de distribuição de energia elétrica na memória do computador. As meta-heurísticas implementadas foram a Greedy Rand... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as near real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, as well as high requirements for up-to-dateness of analytical query results and data consistency, many instances of integration flows are executed over time. Due to this high load, the performance of the central integration platform is crucial for an IT infrastructure. With the aim of throughput maximization, we propose the concept of multi-flow optimization (MFO). In this approach, messages are collected during a waiting time and executed in batches to optimize sequences of plan instances of a single integration flow. We introduce a horizontal (value-based) partitioning approach for message batch creation and show how to compute the optimal waiting time. This approach significantly reduces the total execution time of a message sequence and hence, it maximizes the throughput, while accepting moderate latency time.

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Submitted by SBI Biblioteca Digital (sbi.bibliotecadigital@puc-campinas.edu.br) on 2017-08-10T11:55:42Z No. of bitstreams: 1 FABIANA DA SILVA PODELESKI.pdf: 1801192 bytes, checksum: 27ac2ce1c17ed0cdfce39e602146bdc7 (MD5) / Made available in DSpace on 2017-08-10T11:55:42Z (GMT). No. of bitstreams: 1 FABIANA DA SILVA PODELESKI.pdf: 1801192 bytes, checksum: 27ac2ce1c17ed0cdfce39e602146bdc7 (MD5) Previous issue date: 2017-06-29 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / This document proposes a new approach for the restoration of electric power distribution systems by optimal current flow model (OCF). The importance of working with proposals for restoration using OCF is to allow analyzing the problem of restoration by a multiobjective mathematical programming model with linear or quadratic objective function and constraints that represent the network structure of the distribution system. Two objectives are evaluated for the restoration, losses reduction and recomposition time, resulting in a multiobjective programming problem. The proposed restoration action consists of opening and closing of branches in order to transfer loads to areas that are affected by interrupting the power supply. The proposition is directed to the primary distribution networks, characterized by presenting a radial topology and being in a restorative state, when there is a permanent fault. It is also suitable for systems with distributed generation (DG) when the power flow in the branches is no longer unidirectional. The resolution of the problem starts from the prior knowledge of the distribution system (topology and operational levels), the affected region and the possible recomposition resources for restoring the network through OCF model. The objective function of losses can be represented by a linear or a quadratic function. The linear representation results in a problem with linear equations and inequalities, that is, in a linear programming problem. The use of a quadratic objective function (minimization of losses) implies a more complex model for execution, since it results in a set of linear and non-linear equations and inequalities, when it is a multiobjective problem. The quadratic model may become unsuitable for applications in smart grid technologies due to longer algorithm execution time. The results attested the importance of applying a multiobjective proposal, because when individually evaluated the criteria of loss minimization and shorter recomposition time, different recomposition options were obtained. / O presente documento prop?e um novo enfoque para a recomposi??o de sistemas de distribui??o de energia el?trica resolvido por modelo de Fluxo de Corrente ?timo (FCO). A import?ncia de se trabalhar com propostas para recomposi??o utilizando FCO ? possibilitar a an?lise do problema de recomposi??o por um modelo de programa??o matem?tica multiobjetivo, com fun??o objetivo linear ou quadr?tica e restri??es que representem a estrutura da rede do sistema de distribui??o. S?o avaliados dois objetivos para a recomposi??o, minimiza??o de perdas e menor tempo de recomposi??o, resultando em um problema de programa??o multiobjetivo. A a??o de recomposi??o proposta compreende manobras para transfer?ncia de carga ?s ?reas que se encontram ilhadas devido ? interrup??o de fornecimento de energia. A proposi??o est? dirigida ?s redes prim?rias de distribui??o, caracterizadas por apresentarem topologia radial e se encontrarem em um estado restaurativo, quando h? presen?a de uma falha permanente. Tamb?m ? adequada a sistemas com gera??o distribu?da (GD) quando os fluxos nos ramos deixam de ser unidirecionais. A resolu??o do problema parte do conhecimento pr?vio do sistema de distribui??o (topologia e n?veis operacionais), da regi?o afetada e dos poss?veis recursos restauradores para restaura??o da rede por meio de FCO. A fun??o objetivo pode ser representada por uma fun??o linear ou quadr?tica para as perdas. A representa??o linear resulta em um problema com equa??es e inequa??es lineares, ou seja, em um problema de programa??o linear. A utiliza??o de uma fun??o objetivo quadr?tica (minimiza??o de perdas) implica em um modelo mais complexo para execu??o, uma vez que re?ne um conjunto de equa??es e inequa??es lineares e n?o lineares, quando se tratar de um problema multiobjetivo. O modelo quadr?tico pode se tornar impr?prio para aplica??es em tecnologias de redes inteligentes devido ao maior tempo de execu??o de algoritmo. Os resultados atestaram a import?ncia de aplica??o de uma proposta multiobjetivo, pois quando avaliados individualmente os crit?rios de minimiza??o de perdas e de menor tempo de recomposi??o, foram obtidas diferentes op??es de recomposi??o.

Made available in DSpace on 2014-12-17T14:55:52Z (GMT). No. of bitstreams: 1 BezalielASP_DISSERT.pdf: 2307069 bytes, checksum: aa5ddc5e2ae2722d27d66e85a1e511f1 (MD5) Previous issue date: 2011-08-03 / This work develops a methodology for defining the maximum active power being injected into predefined nodes in the studied distribution networks, considering the possibility of multiple accesses of generating units. The definition of these maximum values is obtained from an optimization study, in which further losses should not exceed those of the base case, i.e., without the presence of distributed generation. The restrictions on the loading of the branches and voltages of the system are respected. To face the problem it is proposed an algorithm, which is based on the numerical method called particle swarm optimization, applied to the study of AC conventional load flow and optimal load flow for maximizing the penetration of distributed generation. Alternatively, the Newton-Raphson method was incorporated to resolution of the load flow. The computer program is performed with the SCILAB software. The proposed algorithm is tested with the data from the IEEE network with 14 nodes and from another network, this one from the Rio Grande do Norte State, at a high voltage (69 kV), with 25 nodes. The algorithm defines allowed values of nominal active power of distributed generation, in percentage terms relative to the demand of the network, from reference values / Neste trabalho, prop?e-se uma metodologia para defini??o dos valores m?ximos de pot?ncia ativa a serem injetados em barras pr?-definidas das redes de distribui??o estudadas, considerando a possibilidade de m?ltiplos acessos de unidades geradoras. A defini??o desses valores m?ximos se obt?m a partir de um estudo de otimiza??o, no qual as novas perdas n?o superam as do caso base, ou seja, sem a presen?a da gera??o distribu?da. No estudo atendem-se as restri??es de carregamentos nos ramos e tens?es do sistema. Para tratar o problema, prop?e-se um algoritmo baseado no m?todo num?rico de otimiza??o nuvem de part?culas, ou particle swarm optimization PSO, aplicado ao estudo de fluxo de carga convencional CA e ao fluxo de carga ?timo para maximiza??o da penetra??o da gera??o distribu?da. Tamb?m se incorporou o m?todo de Newton-Raphson, como alternativa, para a resolu??o do fluxo de carga. Realiza-se a programa??o computacional no software SCILAB. Testa-se o algoritmo proposto com os dados da rede IEEE-14 barras e de uma rede de distribui??o em alta tens?o (69 kV) do Estado do Rio Grande do Norte, com 25 barras. O algoritmo determina valores permitidos de pot?ncia ativa nominal de gera??o distribu?da, em termos percentuais relativos ? demanda da rede, a partir de valores de refer?ncia