As atividades de planejamento de médio prazo (nível tático) são especialmente importantes em empresas de manufatura, visto que tratam do dimensionamento dos recursos produtivos (recursos humanos, materiais, equipamentos, instalações, etc) , que terá impacto na capacidade de atendimento da demanda e nos resultados operacionais da empresa. Este trabalho apresenta um modelo de Planejamento Agregado da Produção e Distribuição para aplicação em empresas do setor cimenteiro, com múltiplas famílias de produtos, múltiplas localidades (de produção e de demanda) e produção em dois estágios, tendo por objetivo a maximização do resultado operacional. O modelo desenvolvido é baseado em Programação Linear Inteira Mista e considera, além dos custos das operações de produção e de transporte, os gastos com impostos, que variam de acordo com a estratégia de abastecimento dos pontos de demanda, dada a existência de diferenças nas alíquotas do Imposto sobre Circulação de Mercadorias e Serviços entre operações inter e intra-estaduais. Os resultados obtidos demonstraram a importância da abordagem conjunta dos processos de produção e de distribuição, tornando clara a necessidade de integração dos mesmos no planejamento agregado num ambiente produtivo de múltiplas localidades e com mais de um processo de produção. / Middle-term production planning (tactical level) is an important activity in manufacturing companies, once it deals with production resources (work-force and production capacity) and stocks dimensioning, impacting on the company supply capacity and on its operating results. This dissertation presents an Aggregate Production and Distribution Planning model to be applied in multi-site, multi-product cement companies with the purpose of maximize operational results. The developed model is based upon Mixed Integer Linear Programming (MILP), and it considers, besides production and transportation operational costs, taxes expenses, which in the Brazilian context can change considerably depending on the supply strategy adopted, due to taxation differences between inter and intra-state sales operations. The results confirmed the importance of the combined approach of the production and distribution, demonstrating the relevance of integrating these two planning processes in a multi-site two-stage production environment.

As atividades de planejamento de médio prazo (nível tático) são especialmente importantes em empresas de manufatura, visto que tratam do dimensionamento dos recursos produtivos (recursos humanos, materiais, equipamentos, instalações, etc) , que terá impacto na capacidade de atendimento da demanda e nos resultados operacionais da empresa. Este trabalho apresenta um modelo de Planejamento Agregado da Produção e Distribuição para aplicação em empresas do setor cimenteiro, com múltiplas famílias de produtos, múltiplas localidades (de produção e de demanda) e produção em dois estágios, tendo por objetivo a maximização do resultado operacional. O modelo desenvolvido é baseado em Programação Linear Inteira Mista e considera, além dos custos das operações de produção e de transporte, os gastos com impostos, que variam de acordo com a estratégia de abastecimento dos pontos de demanda, dada a existência de diferenças nas alíquotas do Imposto sobre Circulação de Mercadorias e Serviços entre operações inter e intra-estaduais. Os resultados obtidos demonstraram a importância da abordagem conjunta dos processos de produção e de distribuição, tornando clara a necessidade de integração dos mesmos no planejamento agregado num ambiente produtivo de múltiplas localidades e com mais de um processo de produção. / Middle-term production planning (tactical level) is an important activity in manufacturing companies, once it deals with production resources (work-force and production capacity) and stocks dimensioning, impacting on the company supply capacity and on its operating results. This dissertation presents an Aggregate Production and Distribution Planning model to be applied in multi-site, multi-product cement companies with the purpose of maximize operational results. The developed model is based upon Mixed Integer Linear Programming (MILP), and it considers, besides production and transportation operational costs, taxes expenses, which in the Brazilian context can change considerably depending on the supply strategy adopted, due to taxation differences between inter and intra-state sales operations. The results confirmed the importance of the combined approach of the production and distribution, demonstrating the relevance of integrating these two planning processes in a multi-site two-stage production environment.

Flights and revisions planning is the problem of assigning flights and revisions to aircrafts in such a way that constraints are satisfied, and some objective function is optimized. Objective function can be to minimalize the number of aircrafts. The intention of my thesis is to find the optimum solution in the problem of flights and revisions planning to aircrafts. In this work, I am using linear programming, which is important and irreplaceable in everyday practical use.

Doctor of Philosophy / Department of Agricultural Economics / Jeffrey M. Peterson / This study focused on the economic efficiency of Kansas farms. The goal was to investigate factors and how they might affect farms and their economic and production performance. Kansas was selected as the region of study for its large agricultural production and distinctive type of multiple-operation farms. Farms in the sample could produce three outputs, crops, livestock and custom work. Inputs for the farms included measures of capital, labor, land and purchased inputs. Production outputs were measured in bushels and tons; input quantities were computed from input expenditures applying an input price index taken from the US Department of Agriculture in real US dollars. The dataset consisted of a 10-year (1998-2007) panel of 456 multi-output farms belonging to the Kansas Farm Management Association (KFMA). Data Envelopment Analysis (DEA) techniques were used to construct a non-parametric efficiency frontier and calculate technical efficiency (TE), allocative efficiency (AE), scale efficiency (SE), and overall or economic efficiency (OE) for each farm and each year. A discretionary input oriented DEA technique was used to assess the effect of capital availability as a farm input and its impact on farms' efficiencies. Efficiency scores in this problem were compared to the farms' scores when the level of debt was accounted for as a farm input. Panel data Tobit analysis was applied to the farms' inefficiency scores to investigate the causality of selected farm characteristics on technical, allocative, scale and overall inefficiencies. For the sampled farms and period, results confirmed that larger farms were more efficient than smaller ones. Farms specializing in livestock products, such as dairy and beef, were reported to be slightly more overall efficient than crop or mixed farms. Some economies of scope were found between custom work operations and crops. Financial structure of the farms was measured using the ratio of total debt to total assets for each farm. According to the results, larger leverage ratios increased all farm efficiencies. The positive effect of debt or capital availability in Kansas farms efficiencies was confirmed. The results of the technical efficiency discretionary DEA model agreed with this finding.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. A mathematical model based on the Multiple Cell Approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. A mathematical model based on the Multiple Cell Approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-07-11T12:50:27Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DissertaçãoRodrigoNunesdeMelo.pdf: 5589879 bytes, checksum: e851e203ef8b11b9711c04c1c34a7ede (MD5) / Made available in DSpace on 2017-07-11T12:50:27Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DissertaçãoRodrigoNunesdeMelo.pdf: 5589879 bytes, checksum: e851e203ef8b11b9711c04c1c34a7ede (MD5) Previous issue date: 2016-06-17 / FACEPE / O planejamento da operação de um sistema elétrico de potência está diretamente relacionado com o despacho de usinas hidrelétricas e termelétricas. As características geográficas do Brasil contribuem para que o parque gerador seja predominantemente hidráulico. Devido à grande dimensão dos sistemas elétricos, a otimização do problema de despacho hidrotérmico é uma tarefa extremamente complexa que pode ser realizada de modo eficiente, buscando otimizar a operação dos reservatório das usinas hidráulicas, onde o objetivo é a redução do custo na geração térmica necessária para atendimento à carga e eventuais déficits de energia, além de maior nível de segurança. O presente trabalho aborda o desenvolvimento e a implementação de um software para resolução do problema do despacho hidrotérmico em três patamares de carga a ser atendido. Neste trabalho o problema de despacho foi formulado como um problema de programação linear, que por sua vez foi solucionado pelos métodos de pontos interiores primal-dual e preditor-corretor de barreira logarítmica. O trabalho faz uma avaliação do desempenho computacional dos métodos implementados e do método LINPROG presente no software Matlab® na solução do problema de planejamento da operação em larga escala, para horizontes de cinco e de dez anos. As simulações foram feitas baseados em dados do Plano Decenal de Energia (PDE) 2022 e apresentaram desempenhos satisfatórios. / The operational planning of electric power systems is directly related to the dispatch of hydroelectric and thermal power plants. The Brazilian electric energy park is a predominantly hydraulic system, due to its geographic characteristics. Due to the large size of the electrical systems, the optimization of the hydrothermal dispatch problem is an extremely complex task that can be carried out efficiently, seeking to optimize the operation of the reservoir in the hydroelectric plants aimed at reducing the cost of the necessary thermal generation to meet the load and possible energy deficits, and a high level of security. This work discusses the development and implementation of a software to solve the hydrothermal dispatch problem in three load steps. In this dissertation the hydrothermal dispatching problem is formulated as a linear programming program, which in term is solved by the following methods of interior point: primal-dual and predictor-corrector with logarithmic barrier. This work provides an evaluation of the computational performance of the implemented methods and LINPROG, presents in the software Matlab®, to solve a large scale operational planning problem, for horizons of five and ten years. The simulation were made based on data from the “Plano Decenal de Energia (PDE) 2022” and showed satisfactory performance.

O planejamento da operação energética do sistema interligado nacional brasileiro é realizado por uma cadeia de modelos computacionais de otimização e simulação da operação. Entretanto, o risco de déficit, um importante indicador de segurança energética no setor elétrico, é tratado como uma variável de saída dos modelos computacionais. No planejamento de médio prazo é utilizado o software NEWAVE, que utiliza uma representação agregada em subsistemas equivalentes. Este trabalho propõe a implementação de um modelo de otimização linear para o planejamento da operação de médio prazo capaz de considerar o risco de déficit em sua formulação. Para o controle de risco de déficit, é proposta a utilização da métrica de risco conhecida por CVaR (Conditional Value at Risk), por se caracterizar como uma métrica de risco coerente, além de poder ser implementada por meio de um conjunto de restrições lineares. / The energetic operation planning of the Brazilian interconnected system is performed by a chain of computational models for the system optimization and simulation. However, the deficit risk, an important energy security indicator for the electric sector, is treated as an output variable on the computational models. In the medium-term of the energetic planning is used the software NEWAVE, which uses equivalent systems on aggregated representation. This work proposes the implementation of a linear optimization model for the medium-term of the energetic planning able to consider the deficit risk in its own formulation. To control the deficit risk is proposed the use of the risk metric known as CVaR (Conditional Value at Risk), because it is characterized as a coherent risk metric, and can be implemented through a set of linear constraints.

O planejamento da operação energética do sistema interligado nacional brasileiro é realizado por uma cadeia de modelos computacionais de otimização e simulação da operação. Entretanto, o risco de déficit, um importante indicador de segurança energética no setor elétrico, é tratado como uma variável de saída dos modelos computacionais. No planejamento de médio prazo é utilizado o software NEWAVE, que utiliza uma representação agregada em subsistemas equivalentes. Este trabalho propõe a implementação de um modelo de otimização linear para o planejamento da operação de médio prazo capaz de considerar o risco de déficit em sua formulação. Para o controle de risco de déficit, é proposta a utilização da métrica de risco conhecida por CVaR (Conditional Value at Risk), por se caracterizar como uma métrica de risco coerente, além de poder ser implementada por meio de um conjunto de restrições lineares. / The energetic operation planning of the Brazilian interconnected system is performed by a chain of computational models for the system optimization and simulation. However, the deficit risk, an important energy security indicator for the electric sector, is treated as an output variable on the computational models. In the medium-term of the energetic planning is used the software NEWAVE, which uses equivalent systems on aggregated representation. This work proposes the implementation of a linear optimization model for the medium-term of the energetic planning able to consider the deficit risk in its own formulation. To control the deficit risk is proposed the use of the risk metric known as CVaR (Conditional Value at Risk), because it is characterized as a coherent risk metric, and can be implemented through a set of linear constraints.

Nous présentons les enjeux du management environnemental et soulignons l’importance d’une politique d’économie d’énergie pour les entreprises. Nous proposons un modèle pour déterminer le bilan énergétique de la fabrication en intégrant les différentes phases productives et non-productives. Nous définissons un double objectif pour la minimisation de la durée de production et de la consommation d’énergie. Nous appliquons ce modèle à l’ordonnancement d’ateliers job-shop flexibles. Pour déterminer la solution optimale nous utilisons deux classes de méthodes : - La première relève des algorithmes génétiques. Nous proposons différents types d’algorithmes pour résoudre ce problème multicritère. Nous proposons par exemple de faire évoluer deux populations pour minimiser respectivement l’énergie consommée et la durée de production et de les croiser pour atteindre l’objectif global. - La seconde relève de la programmation sous contrainte. Nous proposons de rechercher la solution optimale en développant une double arborescence pour évaluer l’énergie consommée et la durée de production. Nous construisons notre algorithme en partant des tâches à réaliser sur les machines ou en partant des machines qui réaliseront les tâches. Nous discutons de la construction du front de Pareto pour l’obtention de la meilleure solution.Nous terminons en comparant les différentes approches et en discutant leur pertinence pour traiter des problèmes de différentes tailles. Nous proposons également plusieurs améliorations et quelques pistes pour de futures recherches. / We present the challenges of environmental management and underline the importance of an energy saving policy for companies. We propose a model to determine the energy balance of manufacturing by integrating the different productive and non-productive phases. We define two purposes for minimizing production time and energy consumption. We apply this model to the scheduling of flexible job-shop workshops. To determine the optimal solution we use two types of methods: - The first is genetic algorithms. We propose different types of algorithms to solve this multi-criteria problem. For example, we propose to develop two populations to minimize the energy consumed and the production time, and to cross them to achieve the overall objective. - The second is constraint programming. We propose to find the optimal solution by developing a double tree to evaluate the energy consumed and the production time. We build our algorithm starting from the tasks to be performed on the machines or from the machines that will perform the tasks. We discuss the construction of the Pareto front to get the best solution.We finish by comparing the different approaches and discussing their relevance to deal with problems of different sizes. We also offer several improvements and some leads for future research.