A relevância da infraestrutura de transporte para incrementos em produtividade, induzindo ao desenvolvimento socioeconômico de determinada região é amplamente reconhecida. O Brasil, no entanto, apresenta sérias deficiências em relação à sua infraestrutura de transporte, oriundas de seu desenvolvimento histórico e da redução de níveis de investimentos públicos no setor nas últimas décadas. Estas deficiências traduzem-se em grande concentração no modal rodoviário para o transporte de cargas, menos eficiente do que os modais ferroviário e hidroviário, resultando em reduzida competitividade das organizações nacionais. Neste contexto, objetivou-se no presente trabalho a proposição de um modelo para avaliação e seleção de investimentos em infraestrutura de transporte de cargas, considerando-se seu caráter multidimensional. Para isso, foi proposta metodologia integrando os conceitos de análise de decisão multicritério e de programação matemática, representados pela teoria de utilidade multiatributo (Multi-Attribute Utility Theory - MAUT) e por problema de otimização de fluxos em rede (Minimum Cost Network Flow Problem - MCNFP), respectivamente. No desenvolvimento do modelo foram contemplados critérios de avaliação referentes às dimensões de análise financeira, operacional e ambiental. Posteriormente, foi considerada a aplicação de versões do modelo proposto com diferentes números de períodos de análise em caso ilustrativo, representativo da rede de transporte disponível e planejada da região Norte do Brasil. A aplicação das diversas versões do modelo proposto, de forma geral, apresentou resultados compatíveis com as teorias relacionadas à avaliação deste problema de decisão, incluindo indução à multimodalidade. Algumas versões do modelo apresentaram violações em algumas de suas restrições. Estes resultados adversos não foram plenamente eliminados, devido a limitações das ferramentas adotadas para aplicação. No entanto, foi possível a correção manual destas violações, resultando em soluções viáveis que, apesar de não serem consideradas ótimas, são mais completas do que soluções obtidas por meio de metodologias unidimensionais de análise. Por fim, foram apresentadas recomendações para condução de trabalhos futuros visando eliminação dos resultados adversos do modelo proposto e complementação de sua análise. / The role of transport infrastructure in productivity increases leading to regional social-economic development is widely recognized. Brazil, however, has serious deficiencies in its transport infrastructure, rooted in the country\'s historical development and in the recent decades\' reduction of public investment in the sector. These deficiencies can be observed in Brazil\'s strong focus on roads for cargo transportation, which besides being less efficient than rail and waterways, results in reduced competitiveness of national enterprises. In this context, the aim of this work was to propose a model for evaluating and selecting investments in freight\'s transportation infrastructure, considering its multidimensional character. It was proposed a methodology integrating the concepts of multi-criteria decision analysis and mathematical programming, represented by the Multi-Attribute Utility Theory (MAUT) along with the Minimum Cost Network Flow Problem (MCNFP). The developed model included financial, operational and environmental analysis evaluation criteria. Subsequently, this study applied the proposed model into a case study of the transportation network, available and planned, of the Northern region of Brazil. Overall, the application of various versions of the proposed model yielded results consistent with related evaluation and decision making theories, including induction of multimodality. Some versions of the model presented some violations of its restrictions. These adverse results were not fully eliminated due to the limitations of the application tools utilized. It was possible, however, to manually correct these violations and obtain viable solutions that, while cannot be considered optimal, are more complete than those obtained by single dimension analysis. Finally, recommendations were made for future studies aiming at eliminating the proposed model\'s adverse outcomes, and complementing its analysis.

This research assesses the impacts of developing shale gas in the UK, with the focus of determining whether or not it is possible to develop it sustainably and how it could affect the electricity and gas mix. There is much uncertainty on the impacts of developing shale gas in the UK, as the country is currently in the early stages of exploration drilling and the majority of studies which have been carried out to analyse the effects of shale gas development have been US specific. To address these questions, the environmental, economic and social sustainability have been assessed and the results integrated to evaluate the overall sustainability. The impacts of shale gas electricity have been assessed so that it can be compared with other electricity generation technologies (coal, nuclear, renewables etc.), to ascertain its impacts on the UK electricity mix. Life cycle assessment is used to evaluate the environmental sustainability of shale gas electricity (and other options), while life cycle costing and social sustainability assessment have been used to evaluate the economic and social sustainability. Multi-criteria decision analysis has been used to combine the results of three to evaluate the overall sustainability. The incorporation of shale gas into the UK electricity mix is modelled in two future scenarios for the year 2030. The scenarios compare different levels of shale gas penetration: low and high. The results show that shale gas will have little effect on improving the environmental sustainability and energy security of the UK’s electricity mix, but could help ease energy prices. In comparison with other options, shale gas is not a sustainable option, as it has higher environmental impacts than the non-fossil fuels and conventional gas and liquefied natural gas: 460 g CO2-Eq. is emitted from the shale gas electricity life cycle, while conventional gas emits 420 g CO2-Eq. and wind 12 g CO2-Eq. The power plant and drilling fluid are the main impact hot spots in the life cycle, while hydraulic fracturing contributes a small amount (5%). In addition to this, there are a number of social barriers which need to be addressed, notably: traffic volume and congestion could increase by up to 31%, public support is low and wastewater produced from hydraulic fracturing could put strain on wastewater treatment facilities. However, the results indicate that shale gas is economically viable, as the cost of electricity is cheaper than solar photovoltaic, biomass and hydroelectricity (9.59 p/kWh vs 16.90, 11.90 and 14.40 p/kWh, respectively). The results of this thesis show that there is a trade-off in the impacts, but because of its poor environmental and social ratings shale gas is not the best option for UK electricity. The results also identify areas for improvement which should be targeted, as well as policy recommendations for best practice and regulation if shale gas were to be developed in the UK.

En sciences économiques, l'une des questions centrales concerne l'allocation des ressources rares et plus particulièrement leur répartition. La décision apparait ainsi au cœur des thématiques économiques, que ce soit en micro-économie ou en macro-économie. Dans un premier temps, nous revenons sur le fait que l’information, élément central de la prise de décision, est imparfaite. En utilisant la théorie des ensembles flous, qui a pour objet de capturer l’imprécision, nous construisons un nombre flou nommé nombre flou C-Shape qui permet de capter la sensibilité du preneur de décision. Nous étudions ensuite la théorie de la décision au travers de deux axes de recherche à savoir (1) la recherche opérationnelle couplée à la théorie des jeux et (2) l’aide à la décision. En premier lieu, Nous faisons une analogie entre la fonction distance et la fonction d’appartenance. Grâce à l’hypothèse de B-convexité et à la fonction C-Shape nous construisons des classes de jeux pour lesquels les joueurs peuvent être optimistes, pessimistes ou neutres, et pour lesquels l’existence d’équilibre de Nash est avérée. Enfin, concernant l’aide à la décision, nous utilisons la fonction C-Shape pour caractériser un nouveau type de critère nommé pseudo critère C-Shape qui permet de considérer les alternatives comme substituables. Ceci permet de prendre en compte, par exemple le contexte institutionnel dans lequel la prise de décision est prise. / Determining the allocation and the distribution of scarce resources is fundamental in economics. Thus, decision theory is the cornerstone of economic theory. In this thesis, we first provide a state of the art insisting on the fact that information, that is a central element of decision-making, is imperfect. Secondly, using fuzzy set theory, which aims to capture imprecision, we construct a fuzzy number, so-called C-Shape that captures the sensitivity of the decision-maker. Thirdly, we study decision theory through two key concepts of operation research: (1) game theory and (2) multi-criteria decision making. We provide an analogy between the gauge functions of convex sets and the membership functions arising in fuzzy set theory. Coupling a suitable notion of -convexity with the C-Shape function, we introduce a class of games for which the players can be optimistic, pessimistic or neutral. In addition the existence of Nash equilibrium is proved for such a class of games. Finally, concerning multi-criteria decision analysis, we use the C-Shape functions to characterize a new type of criteria called C-Shape pseudo-criterion, which makes possible to consider the alternatives as substitutable. This should be of interest to take into account, for example, the institutional context in which decision-making is taken.

Our country is a risky position in terms of natural disasters. In the long run, preferentially settlement areas were selected to ensure maximum benefits in terms of both economic and security aspects, other criteria is not taken account when selection of sites. The main purpose of this study is to examine and compare the properties of settlement location based on natural hazard and environmental constraints to be able to understand the interaction between the settlements and natural conditions at the regional scale of YeSilirmak Basin. A MCDA was set up with 10 different data layers in two data domains (environmental and natural hazards domains), are evaluated. The results of the MCDA scores are then transferred to settlement databases in order to evaluate the number of existing settlements in different environmental and natural hazard related suitability classes. It is found that almost 29% of YeSilirmak catchment is environmentally favorable for settlement, and in coherence with that 41% of all existing settlements are located in this zone, indicating a clear preference among the perception of environmentally better places to be settled in. On the other hand with respect to the natural hazards dataset, the locations of the settlements fail to create any preference, as 73,32% of the area is used by 73,50% of existing settlements, which indicates that the perception of natural hazards are low and do not effect settlement criteria, while the acceptable risk of community is high.

New Zealand faces a range of food safety hazards. Microbial hazards alone were estimated to cause over 2,000 years of lost healthy life in 2011 (Cressey, 2012) and $62m in medical costs and lost productivity in 2009 (Gadiel & Abelson, 2010). Chemical hazards are thought to be well managed through existing controls (Vannoort & Thomson, 2009) whereas microbial hazards are considered harder to control, primarily due to their ability to reproduce along the food production chain. Microbial hazards are thought to cause the majority of acute foodborne gastroenteritis. This research reviewed food safety literature and official documentation, and conducted 55 interviews, mostly with food safety experts from different stakeholder groups, to examine the food safety decision-making environment in New Zealand. This research explores the concept of the ‘stakeholder’ in the context of food safety decision-making and proposes an inclusive ‘stakeholder’ definition as any group which is able to affect, or be affected by, the decision-making process. Utilising this definition, and guided by interviews, New Zealand stakeholders in food safety decision-making were identified and classified as follows: •Regulators •Public health authorities •Food safety scientists/academics •Consumers •Māori •Food Businesses (further classified as): o Farmers o Processors o Food retailers o Exporters Interviews with stakeholders from these groups highlighted twelve criteria as being relevant to multiple groups during food safety intervention evaluation: •Effectiveness •Financial cost •Market Access •Consumer Perceptions •Ease of Implementation •Quality or Suitability •Quality of Science •Equity of Costs •Equity of Benefits •Workplace Safety •Cultural Impact •Animal Welfare There are a number of different ways to measure or assess performance on these criteria. Some are able to be quantitatively measured, while others may require the use of value judgements. This thesis used the Disability-Adjusted Life Year (DALY) metric for quantifying effectiveness during the testing of different MCDA models. This thesis reviews the MCDA process and the food safety specific MCDA literature. There are different ways of conducting MCDA. In particular, there are a large number of models available for the aggregation phase; the process of converting model inputs, in the form of criteria scores and weights, into model recommendations. This thesis has described and reviewed the main classes of model. The literature review and interview process guided the construction and testing of three classes of MCDA model; the Weighted Sum, Analytic Hierarchy Process (AHP) and PROMETHEE models. These models were selected due to their having different characteristics and degrees of complexity, as well as their popularity in the food safety and Health Technology Assessment (HTA) literature. Models were tested on the problem of selecting the most appropriate intervention to address the historic Campylobacter in poultry problem in New Zealand during the mid-2000s. Experimentation was conducted on these models to explore how different configurations utilise data and produce model outputs. This experimentation included: •Varying the format of input data •Exploring the effects of including/excluding criteria •Methods for sensitivity analysis •Exploring how data inputs and outputs can be elicited and presented using visual tools • Creating and using hybrid MCDA models The results of this testing are a key output of this thesis and provide insight into how such models might be used in food safety decision-making. The conclusions reached throughout this research phase can be classified into one of two broad groups: •Those relating to MCDA as a holistic process/methodology for decision-making •Those relating to the specific models and mathematical procedures for generating numerical inputs and outputs This thesis demonstrates that food-safety decision-making is a true multi-criteria, multi-stakeholder problem. The different stakeholders in food-safety decision-making do not always agree on the value and importance of the attributes used to evaluate competing intervention schemes. MCDA is well suited to cope with such complexity as it provides a structured methodology for the systematic and explicit identification, recording and aggregation of qualitative and quantitative information, gathered from a number of different sources, with the output able to serve as a basis for decision-making. The MCDA models studied in this thesis range from models that are simple and quick to construct and use, to more time consuming models with sophisticated algorithms. The type of model used for MCDA, the way these models are configured and the way inputs are generated or elicited can have a significant impact on the results of an analysis. This thesis has identified a number of key methodological considerations for those looking to employ one of the many available MCDA models. These considerations include: •Whether a model can accommodate the type and format of input data •The desired degree of compensation between criteria (i.e. full, partial or no compensation) •Whether the goal of an analysis is the identification of a ‘best’ option(s), or the facilitation of discussion, and communication of data •The degree of transparency required from a model and whether an easily understood audit trail is desired/required •The desired output of a model (e.g. complete or partial ranking). This thesis has also identified a number of practical considerations when selecting which model to use in food safety decision-making. These include: •The amount of time and energy required of stakeholders in the generation of data inputs (elicitation burden) •The degree of training required for participants •How data inputs are to be elicited and aggregated in different group decision-making environments •The availability of MCDA software for assisting an analysis Considering the above points will assist users in selecting a suitable MCDA model that meets their requirements and constraints. This thesis provides original and practical knowledge to assist groups or individuals looking to employ MCDA in the context of food-safety intervention decision-making. This research could also serve as a guide for those looking to evaluate a different selection of MCDA models.

Currently several disparate and incomplete approaches are being used to analyse and make decisions on the complex methodology of carbon capture and storage (CCS). A literature review revealed that, as CCS is a new and complex technology, there is no agreed-upon thorough assessment method for high-level CCS decisions. Therefore, a risk model addressing these weaknesses was created for assessing complex CCS decisions using a multi-criteria decision analysis approach (MCDA). The model is aimed at transparently and comprehensively assessing a wide variety of heterogeneous CCS criteria to provide insights into and to aid decision makers in making CCS-specific decisions. The risk model includes a variety of tools to assess heterogeneous CCS criteria from the environmental, social, economic and engineering fields. The model uses decision trees, sensitivity analysis and Monte Carlo simulation in combination with utility curves and decision makers’ weights to assess decisions based on data and situational uncertainties. Elements in the model have been used elsewhere but are combined here in a novel way to address CCS decisions. Three case studies were developed to run the model in scenarios using expert opinion, project-specific data, literature reviews, and engineering reports from Alberta, Saskatchewan and Europe. In collaboration with Alberta Innovates Technology Futures, a pilot study was conducted with CCS experts in Alberta to assess how they would rank the importance of CCS criteria to a project selection decision. The MCDA model was run using experts’ criteria weights to determine how CCS projects were ranked by different experts. The model was well received by the CCS experts who believed that it could be adapted and commercialized to meet many CCS decision problems. The survey revealed a wide range in experts’ understanding of CCS criteria. Experts also placed more emphasis on criteria from within their field of expertise, although economic criteria dominated weights overall. The results highlight the benefit of a model that clearly demonstrates the trade-offs between projects under uncertain conditions. The survey results also revealed how simple decision analyses can be improved by including more transparent methods, interdisciplinary criteria and sensitivity analysis to produce more comprehensive assessments.

Community benefit schemes in the context of wind power are increasingly provisioned by developers as a means of generating local socio-economic and environmental value, fostering social relations and strengthening acceptance. Determining an appropriate and effective benefit scheme can prove challenging, given the variation of exposed stakeholders, diversity in schemes and the lack of decision making guidance. A multi-criteria decision aid framework for identifying the most appropriate scheme(s) for a hypothetical wind power project is developed. The framework is based on AHP and PROMETHEE II decision support tool, where six (6) alternative schemes are assessed using the preferences of five (5) stakeholders and their relevant criteria. The framework was applied to a fictitious development on the island of Gotland. Results from the applied example indicate that the most locally suited outcome was the ownership based models. It is anticipated that the methodological framework can help identify the scheme(s) that respond to the needs and preferences of the locality. Moreover, a decision making platform of this nature can provide practical support to developers, communities and local authorities, and contribute to a more effective and efficient development and negotiation process surrounding community benefit schemes.

The aim of this thesis is to compare the theoretical results of evaluation of current accounts with the practical results of the survey. In the theoretical part were described the selected methods of multi-criteria decision analysis, that makes it possible to evaluate current accounts according to several criterias. It was also carried out a survey which aimed to identify whicg current accounts are used by respondents and what are the conditions for their use. In the practical part were a compared of theoretical and practical results. In conclusion are evaluated the best products and compiled an overview of evaluation accounts used by respondents.

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No. of bitstreams: 1 DissFAY.pdf: 4500500 bytes, checksum: f53692d6c8e17f50aa45ea1cbe9444e2 (MD5) Previous issue date: 2015-04-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This study, conducted in the limits of EPA of Botucatu, sought by the spatial distribution of soil strength, prepare an environmental fragility index of soil resistance and using methodologies such as the analysis of environmental fragility, geostatistics and multi-criteria decision analysis generate a environmental fragility map of the study object. The sample area was divided into 78 quadrants where data were collected on the soil resistance with an impact penetrometer, slope, soil types and land use and vegetation cover. It has been prepared an environmental fragility index for soil strength and weaknesses and maps were generated for each parameter and raised by map algebra in a multi-criteria decision analysis, the preparation of the environmental fragility map. The results can be noted that the slope, the lowest average of soil resistance were found in the higher slope (2,38 MPa - 20-40% and 2,92 MPa - greater than 40%) and this group showed significant differences compared to other classes of minor steepness (6.44 MPa to 6%, 7.11 MPa 6 to 12 MPa and 4.85% from 12 to 20%) at a depth of 0 to 20cm. There were significant differences between the different types of land use and vegetation cover. Sugarcane (9,35 MPa) and grazing (9,05 MPa) had the highest average resistance, differing from citrus (6.23 MPa) that differed from perennial crops such as eucalyptus forests (5,28 MPa), the coffee (3,58 MPa) and native forests (2,33 MPa) that formed the last group of least resistance in the layer 0-20 cm soil. In the spatial distribution of soil resistance, ordinary kriging in the exponential semivariogram model proved the most efficient method, given the parameters of the cross validation compared with gausian and spherical models. The multi-criteria decision analysis, through map algebra, generated the final product of this study, the map of environmental fragility to the EPA of Botucatu which showed that 32.43% of the total sample area are within lower fragility environmental classes and 67.57% of the total sample area are averaged fragility classes to very high fragility. / Esse estudo, realizado no limites da APA de Botucatu, buscou, através da distribuição espacial da resistência do solo, elaborar um índice de fragilidade ambiental da resistência do solo e, utilizando metodologias como a análise da fragilidade ambiental, a geoestatistica e a análise de decisão multicritérios gerar uma carta ou mapa de fragilidade ambiental para o objeto de estudo. Dividiu-se a área amostral em 78 quadrantes onde foram coletados dados sobre a resistência do solo com um penetrômetro de impacto, dados sobre a declividade, os tipos de solos e uso do solo e cobertura vegetal. Foi elaborado um índice de fragilidade ambiental para a resistência do solo e foram geradas cartas de fragilidades para cada parâmetro levantado e através da álgebra de mapas em uma análise de decisão de multicritérios, a elaboração da carta de fragilidade ambiental. Como resultados pode-se destacar que na declividade, as menores médias da resistência do solo foram encontradas nas declividades mais altas (2,38 MPa - de 20 a 40% e 2,92 MPa - maior que 40%) e esse grupo apresentou diferenças significativas se comparadas com as outras classes de menores declividades (6,44 MPa até 6%, 7,11 MPa de 6 a 12% e 4,85 MPa de 12 a 20%), na profundidade de 0 a 20cm. Foram encontradas diferenças significativas entre os diferentes tipos de uso do solo e cobertura vegetal. A cana de açúcar (9,35 MPa) e as pastagens (9,05 MPa) apresentaram as maiores resistências médias, diferindo do citros (6,23 MPa) que diferiu das culturas perenes, como as florestas de eucalipto (5,28 MPa), o café (3,58 MPa) e as florestas nativas (2,33 MPa) que formaram o último grupo, de menor resistência na camada 0 a 20 cm do solo. Na distribuição espacial da resistência do solo, a krigagem ordinária no modelo de semivariograma exponencial foi o método mais eficiente, atendendo aos parâmetros da validação cruzada comparado com os modelos gausiano e esférico. A análise de decisão de multicritérios, através da álgebra de mapas, gerou o produto final desse estudo, a carta de fragilidade ambiental para a APA de Botucatu que mostrou que 32,43% do total da área amostral se encontram dentro de classes baixas de fragilidade ambiental e 67,57% do total da área amostral se encontram em classes de fragilidade de média a muito alta.

Com o aumento do número de desastres e consequente incremento no número de pessoas vitimadas, a preparação para esses eventos é uma necessidade das sociedades modernas. Neste sentido, o planejamento das operações logísticas para atendimento as situações de emergências é uma atividade recente e pouco explorada na produção acadêmica. O objetivo deste trabalho é estabelecer uma metodologia para definir locais para o pré-posicionamento de materiais utilizados no socorro a populações afetadas por desastres através de um modelo de otimização estocástica de dois estágios e análise de decisão multicritério e que considerem parâmetros quantitativos e qualitativos. Com base nos custos de transporte e do não atendimento a demanda, e utilizando informações como mapeamentos de riscos; custos de transporte; histórico de ocorrências de desastres; cobertura geográfica; compras de materiais; capacidades de depósitos e de transporte, um modelo estocástico de programação linear minimiza os custos operacionais para abastecimento às vítimas. Uma análise detalhada sobre como atribuir penalidades para demanda não atendida também é apresentada. Devido à incerteza quanto a severidade de um desastre e a influência da mídia nas fases pós-desastres estes parâmetros são representados na forma de cenários. O resultado do modelo estocástico mostra a quantidade de locais e quais localidades minimizam o custo operacional. Após a obtenção desse resultado, uma nova etapa é utilizada para decisão de escolha do local, com a aplicação de modelo de decisão multicritério que considere, além dos valores obtidos pela modelagem, critérios subjetivos característicos a operações humanitárias. Os resultados finais mostram que modelos estocásticos promovem resultados mais confiáveis que os determinísticos, especialmente, em situações nas quais materiais disponíveis não podem atender toda a demanda e que a consideração de critérios qualitativos e quantitativos proporciona uma decisão mais robusta em operações humanitárias. / The increase in disasters and the consequent increase in the number of victims make it highly necessary to prepare for these events in modern societies. Logistics operations planning to meet emergencies is a recent activity and little explored in academic production. Our aim is to establish a method to locate pre-positioned materials used in disaster relief through a two-stage stochastic optimization model and a multi-criteria decision analysis that consider quantitative and qualitative parameters. Based on transportation and unattended demand costs, and using information such as risk mapping, transportation costs, historical occurrences of disasters, coverage, materials purchase, warehouses and transport capacities, a stochastic linear programming model minimizes the operating costs to supply the victims. A detailed analysis on how to assign penalties for unmet demand is also presented. Due to the uncertainty of the disasters severity and the influence of the media in phases after disasters, these parameters are represented as scenarios. The result of the stochastic model shows the quantity and the locations that minimize the operational cost. After this result, a new phase is applied for site selection, with the application of multi-criteria decision analysis that consider the values provided by the model and subjective criteria characteristic of humanitarian operations. The final results show that stochastic models promote more reliable results than deterministic ones, especially in situations in which the materials available cannot meet all the demand and that the consideration of qualitative and quantitative criteria provides better decisions in humanitarian operations.