Emergency department(ED) is one of the busiest clinical units in Winnipeg Victoria Gen-eral Hospital (VGH) which faces the challenge of patients’ long waiting-time as increas-ing healthcare demand and limited resources. This research investigates the critical factors of the ED operation to enhance the operational efficiency using simulation modeling and optimization. The contribution of this research is the integration of simulation and optimization for the performance improvement of ED operations. Discrete-events simula-tion (DES) methodology provides a cost-effective tool to analyse the performance of the ED operations and evaluates the potential alternatives. Design of experiments (DOE) and Scatter search (SS) of model optimization are proposed to search the ED potential capaci-ty for waiting-time reduction. The patient-flow is accelerated along with the waiting-time reduction, which results in better efficient patient throughput in the ED. A specific strate-gy is suggested to improve the ED operation based on the simulation model.

Emergency department(ED) is one of the busiest clinical units in Winnipeg Victoria Gen-eral Hospital (VGH) which faces the challenge of patients’ long waiting-time as increas-ing healthcare demand and limited resources. This research investigates the critical factors of the ED operation to enhance the operational efficiency using simulation modeling and optimization. The contribution of this research is the integration of simulation and optimization for the performance improvement of ED operations. Discrete-events simula-tion (DES) methodology provides a cost-effective tool to analyse the performance of the ED operations and evaluates the potential alternatives. Design of experiments (DOE) and Scatter search (SS) of model optimization are proposed to search the ED potential capaci-ty for waiting-time reduction. The patient-flow is accelerated along with the waiting-time reduction, which results in better efficient patient throughput in the ED. A specific strate-gy is suggested to improve the ED operation based on the simulation model.

O presente trabalho trata da solução para o problema da elaboração de programações de transporte do sistema de distribuição de materiais da Força Aérea Brasileira (FAB). Essas programações de transporte consistem em definir os roteiros de entrega e coleta de materiais a serem realizadas simultaneamente em cada local de entrega/coleta a partir de um centro de distribuição, considerando-se a frota de veículos homogênea. Isto é característico de um Problema de Roteirização de Veículos com Coletas e Entregas Simultâneas (PRVCES). A gestão do sistema de distribuição física da FAB considera a complexidade desse sistema e os dados relativos às demandas de transporte de carga em cada um desses locais para elaborar as programações de transporte. Essas programações são elaboradas tendo em vista os limites de capacidade dos veículos, as características físicas das cargas e as prioridades de embarque. O gestor desse sistema possui boa visibilidade das demandas de transporte, porém, devido à grande quantidade de informações disponíveis e à elevada complexidade desse sistema, é impossível elaborarem-se manualmente programações de transporte que resultem em viagens de distribuição eficientes. O PRVCES foi resolvido por meio da meta-heurística Busca Dispersa (do inglês Scatter Search) integrada com a meta-heurística Descida em Vizinhança Variável (do inglês Variable Neighborhood Descent) utilizada como método de melhoria das soluções. Os resultados superaram ou se igualaram a alguns dos obtidos por outros autores para os mesmos problemas de teste com as mesmas restrições, o que demonstra que a Busca Dispersa implementada é competitiva para solucionar o PRVCES. Quanto à aplicação na FAB, os resultados mostraram que a utilização do método de solução desenvolvido resultará em programações de transporte elaboradas em curto tempo de processamento e que estas incidirão positivamente sobre a eficiência do sistema de distribuição de materiais da FAB. / This work deals with the solution to the problem of drawing up transport schedules in the material distribution system of the Brazilian Air Force (BAF). These transport schedules consist in defining the routes for material pickup and delivery to be accomplished simultaneously in each delivery/pickup location from a distribution center, considering a homogeneous fleet of vehicles. This is characteristic of a Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP). The management of the physical distribution of BAF considers the complexity of this system and the data regarding the cargo transport demands in each one of those locations to draw up transport schedules. These schedules are drawn up regarding the capacity limits of the vehicles, the physical characteristics of the cargoes and the shipping priorities. A good visibility of transport demands in each location is available to the manager of this system, but due to the great quantity of data to deal with and the high complexity of the physical distribution system of BAF, it is impossible to draw up transport schedules that result in efficient distribution trips. The VRPSDP was solved by means of the Scatter Search meta-heuristic integrated with the Variable Neighborhood Descent meta-heuristic as the solution improvement method. The results exceeded or equaled some of those obtained by other authors using the same test problems with the same restrictions, what indicates that the implemented Scatter Search is competitive to solve the VRPSDP. As for the application in the BAF, the results showed that using the solution method developed will result in schedules drawn up in short processing time and focused on the efficiency of the material distribution system of the BAF.

Orientador: Rubén Augusto Romero Lázaro / Banca: José Roberto Sanches Mantovani / Banca: Antonio Padilha Feltrin / Banca: Luiz Carlos Pereira da Silva / Banca: Eduardo Nobuhiro Asada / Resumo: O Problema de Planejamento da Expansão de Sistemas de Transmissão de Energia Elétrica consiste em se escolher, entre um conjunto pré-definido de circuitos candidatos, aqueles que devem ser incorporados ao sistema de forma a minimizar os custos de investimento e operação ao e atender a demanda de energia futura ao longo de um horizonte de planejamento com confiabilidade, assumindo como conhecido o plano de geração. É considerado um problema muito complexo e difícil por se tratar de um problema não linear inteiro misto, não convexo, multimodal e altamente combinatório. Este problema tem sido solucionado usando técnicas clássicas como Decomposição ao de Benders e Branch and Bound, assim como também algoritmos heurísticos e metaheurísticas obtendo diversos resultados, mais com uma série de problemas como, por exemplo, alto esforço computacional e problemas de convergência. Neste trabalho apresentam-se duas novas técnicas de solução para o problema, a saber, as metaheurísticas Busca em Vizinhança Variável e a Busca Dispersa. A Busca em Vizinhança Variável é uma técnica baseada em trocas de estruturas de vizinhança dentro de um algoritmo de busca local, e a metaheurística Busca Dispersa, um método evolutivo que combina sistematicamente conjuntos de soluções para se obter solucões melhores. Essas técnicas de solução oferecem novas alternativas de solução que oferecem solução aos problemas encontrados com outros métodos, como é um baixo esforço computacional é uma melhor convergência, sendo este o principal aporte do trabalho. Os algoritmos são apresentados sistematicamente, explicando os seus algoritmos e a forma como são adaptados para resolver o problema do planejamento da expansão de sistemas de transmissão considerando-se a modelagem matemática conhecida com o modelo de transporte e o modelo DC. São realizados testes com os sistemas... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Electric Energy Transmission Network Expansion Problem consist in choose among a set of pre-defined circuits candidates, who must be incorporated into the system so as to minimize the investment costs and operation and meet the future energy demand over a planning horizon with reliability, assuming the generation plan is known. It is a very complex and difficult problem because it is non linear, non convex, multimodal and highly combinatorial. This problem has been solved using traditional techniques such as Benders decomposition and Branch and Bound, as well as heuristic algorithms and metaheuristics getting different results, but with a series of problems such as high computational effort and convergence problems. This paper tests out two new techniques for solving the problem as are the metaheuristics Variable Neighborhood Search and Scatter Search. The Variable Neighborhood Search is a technique based on trading structures within a neighborhood of a local search algorithm, and the Scatter Search metaheuristic is a method which combines systematically sets of solutions in an evolutionary way to achieve better solutions. These solution techniques offer new alternatives to solve the problems encountered with other methods, such as a low computational effort and better convergence, which is the main contribution of this work. The techniques are presented systematically, explaining their algorithms and the way they are adapted to solve the network expansion planning problem based on the mathematical model known as the transportation model and the DC model. They are tested with the systems Southern Brazilian with 46 buses and the IEEE 24 buses system, results are compared with those obtained with other metaheuristics, obtaining excellent results with a best performance both in processing speed as in computational effort. / Doutor

<p>In the field of systems biology the task of finding optimal model parameters is a common procedure. The optimization problems encountered are often multi-modal, i.e., with several local optima. In this thesis, a class of algorithms for multi-modal problems called metaheuristics are studied. A downside of metaheuristic algorithms is that they are dependent on algorithm settings in order to yield ideal performance.This thesis studies an approach to tune these algorithm settings using user constructed test functions which are faster to evaluate than an actual biological model. A statistical procedure is constructed in order to distinguish differences in performance between different configurations. Three optimization algorithms are examined closer, namely, scatter search, particle swarm optimization, and simulated annealing. However, the statistical procedure used can be applied to any algorithm that has configurable options.The results are inconclusive in the sense that performance advantages between configurations in the test functions are not necessarily transferred onto real biological models. However, of the algorithms studied a scatter search implementation was the clear top performer in general. The set of test functions used must be studied if any further work is to be made following this thesis.In the field of systems biology the task of finding optimal model parameters is a common procedure. The optimization problems encountered are often multi-modal, i.e., with several local optima. In this thesis, a class of algorithms for multi-modal problems called metaheuristics are studied. A downside of metaheuristic algorithms is that they are dependent on algorithm settings in order to yield ideal performance.</p><p>This thesis studies an approach to tune these algorithm settings using user constructed test functions which are faster to evaluate than an actual biological model. A statistical procedure is constructed in order to distinguish differences in performance between different configurations. Three optimization algorithms are examined closer, namely, scatter search, particle swarm optimization, and simulated annealing. However, the statistical procedure used can be applied to any algorithm that has configurable options.</p><p>The results are inconclusive in the sense that performance advantages between configurations in the test functions are not necessarily transferred onto real biological models. However, of the algorithms studied a scatter search implementation was the clear top performer in general. The set of test functions used must be studied if any further work is to be made following this thesis.</p>

In the literature, a large number of metaheuristic search techniques have been proposed up to present time and some of those have been used in structural optimization. Scatter search is one of those techniques which has proved to be effective when solving combinatorial and nonlinear optimization problems such as scheduling, routing, financial product design and other problem areas. Scatter search is an evolutionary method that uses strategies based on a composite decision rules and search diversification and intensification for generating new trial points. Broodly speaking, this thesis is concerned with the use and application of scatter search technique in structural optimization. A newly developed optimization algorithm called modified scatter search is modified which is computerized in a software called SOP2012. The software SOP2012 is integrated with well-known structural analysis software SAP2000 using application programming interface for size optimum design of steel structures. Numerical studies are carried out using a test suite consisting of five real size design examples taken from the literature. In these examples, various steel truss and frame structures are designed for minimum weight according to design limitations imposed by AISC-ASD (Allowable Stress Design Code of American Institute of Steel Construction). The results reveal that the modified scatter search technique is very effective optimization technique for truss structures, yet its performance can be assessed ordinary for frame structures.

In the field of systems biology the task of finding optimal model parameters is a common procedure. The optimization problems encountered are often multi-modal, i.e., with several local optima. In this thesis, a class of algorithms for multi-modal problems called metaheuristics are studied. A downside of metaheuristic algorithms is that they are dependent on algorithm settings in order to yield ideal performance.This thesis studies an approach to tune these algorithm settings using user constructed test functions which are faster to evaluate than an actual biological model. A statistical procedure is constructed in order to distinguish differences in performance between different configurations. Three optimization algorithms are examined closer, namely, scatter search, particle swarm optimization, and simulated annealing. However, the statistical procedure used can be applied to any algorithm that has configurable options.The results are inconclusive in the sense that performance advantages between configurations in the test functions are not necessarily transferred onto real biological models. However, of the algorithms studied a scatter search implementation was the clear top performer in general. The set of test functions used must be studied if any further work is to be made following this thesis.In the field of systems biology the task of finding optimal model parameters is a common procedure. The optimization problems encountered are often multi-modal, i.e., with several local optima. In this thesis, a class of algorithms for multi-modal problems called metaheuristics are studied. A downside of metaheuristic algorithms is that they are dependent on algorithm settings in order to yield ideal performance. This thesis studies an approach to tune these algorithm settings using user constructed test functions which are faster to evaluate than an actual biological model. A statistical procedure is constructed in order to distinguish differences in performance between different configurations. Three optimization algorithms are examined closer, namely, scatter search, particle swarm optimization, and simulated annealing. However, the statistical procedure used can be applied to any algorithm that has configurable options. The results are inconclusive in the sense that performance advantages between configurations in the test functions are not necessarily transferred onto real biological models. However, of the algorithms studied a scatter search implementation was the clear top performer in general. The set of test functions used must be studied if any further work is to be made following this thesis.

O presente trabalho trata da solução para o problema da elaboração de programações de transporte do sistema de distribuição de materiais da Força Aérea Brasileira (FAB). Essas programações de transporte consistem em definir os roteiros de entrega e coleta de materiais a serem realizadas simultaneamente em cada local de entrega/coleta a partir de um centro de distribuição, considerando-se a frota de veículos homogênea. Isto é característico de um Problema de Roteirização de Veículos com Coletas e Entregas Simultâneas (PRVCES). A gestão do sistema de distribuição física da FAB considera a complexidade desse sistema e os dados relativos às demandas de transporte de carga em cada um desses locais para elaborar as programações de transporte. Essas programações são elaboradas tendo em vista os limites de capacidade dos veículos, as características físicas das cargas e as prioridades de embarque. O gestor desse sistema possui boa visibilidade das demandas de transporte, porém, devido à grande quantidade de informações disponíveis e à elevada complexidade desse sistema, é impossível elaborarem-se manualmente programações de transporte que resultem em viagens de distribuição eficientes. O PRVCES foi resolvido por meio da meta-heurística Busca Dispersa (do inglês Scatter Search) integrada com a meta-heurística Descida em Vizinhança Variável (do inglês Variable Neighborhood Descent) utilizada como método de melhoria das soluções. Os resultados superaram ou se igualaram a alguns dos obtidos por outros autores para os mesmos problemas de teste com as mesmas restrições, o que demonstra que a Busca Dispersa implementada é competitiva para solucionar o PRVCES. Quanto à aplicação na FAB, os resultados mostraram que a utilização do método de solução desenvolvido resultará em programações de transporte elaboradas em curto tempo de processamento e que estas incidirão positivamente sobre a eficiência do sistema de distribuição de materiais da FAB. / This work deals with the solution to the problem of drawing up transport schedules in the material distribution system of the Brazilian Air Force (BAF). These transport schedules consist in defining the routes for material pickup and delivery to be accomplished simultaneously in each delivery/pickup location from a distribution center, considering a homogeneous fleet of vehicles. This is characteristic of a Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP). The management of the physical distribution of BAF considers the complexity of this system and the data regarding the cargo transport demands in each one of those locations to draw up transport schedules. These schedules are drawn up regarding the capacity limits of the vehicles, the physical characteristics of the cargoes and the shipping priorities. A good visibility of transport demands in each location is available to the manager of this system, but due to the great quantity of data to deal with and the high complexity of the physical distribution system of BAF, it is impossible to draw up transport schedules that result in efficient distribution trips. The VRPSDP was solved by means of the Scatter Search meta-heuristic integrated with the Variable Neighborhood Descent meta-heuristic as the solution improvement method. The results exceeded or equaled some of those obtained by other authors using the same test problems with the same restrictions, what indicates that the implemented Scatter Search is competitive to solve the VRPSDP. As for the application in the BAF, the results showed that using the solution method developed will result in schedules drawn up in short processing time and focused on the efficiency of the material distribution system of the BAF.

Modeling the liver deformation forms the basis for the development of new clinical applications that improve the diagnosis, planning and guidance in liver surgery. However, the patient-specific modeling of this organ and its validation are still a challenge in Biomechanics. The reason is the difficulty to measure the mechanical response of the in vivo liver tissue. The current approach consist of performing minimally invasive or open surgery aimed at estimating the elastic constant of the proposed biomechanical models. This dissertation presents how the use of medical image analysis and evolutionary computation allows the characterization of the biomechanical behavior of the liver, avoiding the use of these minimally invasive techniques. In particular, the use of similarity coefficients commonly used in medical image analysis has permitted, on one hand, to estimate the patient-specific biomechanical model of the liver avoiding the invasive measurement of its mechanical response. On the other hand, these coefficients have also permitted to validate the proposed biomechanical models. Jaccard coefficient and Hausdorff distance have been used to validate the models proposed to simulate the behavior of ex vivo lamb livers, calculating the error between the volume of the experimentally deformed samples of the livers and the volume from biomechanical simulations of these deformations. These coefficients has provided information, such as the shape of the samples and the error distribution along their volume. For this reason, both coefficients have also been used to formulate a novel function, the Geometric Similarity Function (GSF). This function has permitted to establish a methodology to estimate the elastic constants of the models proposed for the human liver using evolutionary computation. Several optimization strategies, using GSF as cost function, have been developed aimed at estimating the patient-specific elastic constants of the biomechanical models proposed for the human liver. Finally, this methodology has been used to define and validate a biomechanical model proposed for an in vitro human liver. / Martínez Martínez, F. (2014). Determining the Biomechanical Behavior of the Liver Using Medical Image Analysis and Evolutionary Computation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39337 / TESIS

Made available in DSpace on 2014-06-11T19:22:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-04-18Bitstream added on 2014-06-13T18:08:30Z : No. of bitstreams: 1 taglialenha_sls_dr_ilha.pdf: 776756 bytes, checksum: ee3e13f4456bb0d2f6f5faaf48d8309f (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O Problema de Planejamento da Expansão de Sistemas de Transmissão de Energia Elétrica consiste em se escolher, entre um conjunto pré-definido de circuitos candidatos, aqueles que devem ser incorporados ao sistema de forma a minimizar os custos de investimento e operação ao e atender a demanda de energia futura ao longo de um horizonte de planejamento com confiabilidade, assumindo como conhecido o plano de geração. É considerado um problema muito complexo e difícil por se tratar de um problema não linear inteiro misto, não convexo, multimodal e altamente combinatório. Este problema tem sido solucionado usando técnicas clássicas como Decomposição ao de Benders e Branch and Bound, assim como também algoritmos heurísticos e metaheurísticas obtendo diversos resultados, mais com uma série de problemas como, por exemplo, alto esforço computacional e problemas de convergência. Neste trabalho apresentam-se duas novas técnicas de solução para o problema, a saber, as metaheurísticas Busca em Vizinhança Variável e a Busca Dispersa. A Busca em Vizinhança Variável é uma técnica baseada em trocas de estruturas de vizinhança dentro de um algoritmo de busca local, e a metaheurística Busca Dispersa, um método evolutivo que combina sistematicamente conjuntos de soluções para se obter solucões melhores. Essas técnicas de solução oferecem novas alternativas de solução que oferecem solução aos problemas encontrados com outros métodos, como é um baixo esforço computacional é uma melhor convergência, sendo este o principal aporte do trabalho. Os algoritmos são apresentados sistematicamente, explicando os seus algoritmos e a forma como são adaptados para resolver o problema do planejamento da expansão de sistemas de transmissão considerando-se a modelagem matemática conhecida com o modelo de transporte e o modelo DC. São realizados testes com os sistemas... / Electric Energy Transmission Network Expansion Problem consist in choose among a set of pre-defined circuits candidates, who must be incorporated into the system so as to minimize the investment costs and operation and meet the future energy demand over a planning horizon with reliability, assuming the generation plan is known. It is a very complex and difficult problem because it is non linear, non convex, multimodal and highly combinatorial. This problem has been solved using traditional techniques such as Benders decomposition and Branch and Bound, as well as heuristic algorithms and metaheuristics getting different results, but with a series of problems such as high computational effort and convergence problems. This paper tests out two new techniques for solving the problem as are the metaheuristics Variable Neighborhood Search and Scatter Search. The Variable Neighborhood Search is a technique based on trading structures within a neighborhood of a local search algorithm, and the Scatter Search metaheuristic is a method which combines systematically sets of solutions in an evolutionary way to achieve better solutions. These solution techniques offer new alternatives to solve the problems encountered with other methods, such as a low computational effort and better convergence, which is the main contribution of this work. The techniques are presented systematically, explaining their algorithms and the way they are adapted to solve the network expansion planning problem based on the mathematical model known as the transportation model and the DC model. They are tested with the systems Southern Brazilian with 46 buses and the IEEE 24 buses system, results are compared with those obtained with other metaheuristics, obtaining excellent results with a best performance both in processing speed as in computational effort.