How do different stakeholders weigh different criteria in a sociallyimportant decision? When a decision needs to be made in today'ssociety, there are many criteria that can be analyzed. These criteriacan be linked to three main areas: economy, ecology, and socialaspects. This study examines how different stakeholder groupsvalue and prioritize different criteria. The study has focused on theissue of speed limits on the E14 road, aiming to use decisionanalytic models to determine how these stakeholders valuedifferent decisions and how they weigh and prioritize differentcriteria related to this decision. To achieve this goal, interviewswere conducted with two stakeholder groups relevant to the issue,namely the Swedish Transport Administration and themunicipality and region. During the interviews, the respondentsweighed and evaluated the different criteria and how they areinfluenced by a speed reduction. The information from theinterviews was then compiled using multi-attribute value theory.The results of the study show that the two stakeholder groupsview the speed reduction from different perspectives. One groupprimarily considered the issue from a traffic safety standpoint,where economic criteria played a less significant role, while theother group highly valued both traffic safety and economic factors.The results indicate that the speed reduction has an overall positiveimpact based on the analyzed criteria. / Hur viktar sig olika intressenter olika kriterier i ett samhällsviktigtbeslut? När ett beslut i dagens samhälle behöver göras finns detmånga kriterier som det går att analysera beslutet från. Dessakriterier kan kopplas ihop till tre stycken huvudområden:ekonomi, ekologi och sociala. I denna studie studeras hur olikaintressentgrupper värderar och viktar olika kriterier. Arbetet harstuderat frågan om hastighetsbeslut på E14 där målet med studienär att genom användning av beslutsanalytiska modeller därintressenter identifieras ta reda på hur dessa intressenter värderarolika beslut och hur de viktar och värderar olika kriterier kopplattill detta beslut. För att uppnå målet har intervjuer gjorts med tvåolika intressentgrupper som är relaterade till frågan, nämligenTrafikverket och Kommun och Region. Respondenterna har iintervjuerna viktat och värderat de olika kriterierna och hur depåverkas av en hastighetssänkning. Informationen frånintervjuerna har därefter sammanställts genom att använda multiattribute value theory. Resultatet från studien visar att de två olikaintressegrupperna ser hastighetssänkningen från olika perspektiv.Den ena gruppen såg på frågan mer från etttrafiksäkerhetsperspektiv där de ekonomiska kriterierna inte hadenågon större betydande roll, medan den andra gruppen värderadeäven trafiksäkerhet högt samt även de ekonomiska. Resultatetvisar att hastighetssänkningen får totalt sett en positiv inverkanutifrån de kriterier som analyseras.

Nuclear energy is a source of carbon free power. With many countries striving to make deep carbon cuts in their energy sectors, nuclear energy could be a large part of the solution. One of the main obstacles standing in the way of the use of nuclear energy is the issue of used nuclear fuel disposal. According to the NEI, the U.S. creates about 2000 metric tons of used nuclear fuel per year and has generated around 76,000 metric tons of used nuclear fuel over the last 4 decades. While there are technical problems that need to be solved, it is primarily the public and political opposition to the disposal of used nuclear fuel that stands in the way of progress in this area. This work addresses this issue through Multi-Criteria Decision Analysis (MCDA). To make a decision among ten different fuel cycles, we have brought together five stakeholders: Nuclear Scientists and Engineers, Environmental Scientists, Economists, Political Scientists, and The General Public. Using Multi-Attribute Value Theory (MAVT), we have been able to develop decision models for each stakeholder as well as a model that combines them all and came to the conclusion that of the ten fuel cycles considered, the best decision is to continue to use On Site Dry Cask Storage. This decision is made with small sample sizes but the methodology could be applied at much larger scales and can potentially be used to choose a fuel cycle that encounters much less political and social opposition to its implementation.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-11-03T11:54:04Z No. of bitstreams: 1 Murillo da Silveira Grübler_.pdf: 3027339 bytes, checksum: 2fdb175c76ab90e275bf8ba04792e452 (MD5) / Made available in DSpace on 2016-11-03T11:54:04Z (GMT). No. of bitstreams: 1 Murillo da Silveira Grübler_.pdf: 3027339 bytes, checksum: 2fdb175c76ab90e275bf8ba04792e452 (MD5) Previous issue date: 2016-08-19 / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / O Gerenciamento de Leitos é uma importante área de planejamento e controle hospitalar. Sua função é garantir o equilíbrio entre os pacientes que chegam através do setor de emergência, os eletivos que possuem algum tratamento agendado e aqueles que saem do hospital. Dessa forma, esse gerenciamento possibilita manter alta a taxa de ocupação dos quartos, mas sem realmente lotá-los, além de prever qualquer situação não planejada. A gestão eficaz de leitos hospitalares como recurso sempre foi um desafio para os gestores. Nas décadas de 80 e 90, por exemplo, milhares de pacientes tiveram operações canceladas em virtude de razões não médicas. Como há necessidade de um melhor controle do fluxo, a área de Gerenciamento de Leitos começou, então, a receber mais atenção acadêmica e também políticas nacionais para a sua gestão. O processo de admissão e posicionamento de enfermos, a partir do Gerenciamento de Leitos, vem se desenvolvendo nos últimos anos através de diversas técnicas de pesquisa operacional, tais como simulação, teoria de filas, análise estatística, entre outras. Devido às constantes incertezas vividas pelos hospitais atualmente, o uso do modelo cognitivo Ciência de Situação em pesquisas científicas na área da saúde vem crescendo cada vez mais. A Ciência de Situação é uma área de estudo que busca compreender o contexto dos ambientes e projetar ações futura. Em suma, é uma técnica que vai além do tradicional processamento de informações, visto que procura explicar o comportamento humano na operação de sistemas complexos. Nessa assertiva, este trabalho tem como objetivo utilizar a Ciência de Situação na área de Gerenciamento de Leitos, usando um modelo híbrido que une a técnica de Rede Neural Artificial Multilayer Perceptron com a Teoria do Valor Multiatributo para tomada de decisão, auxiliando gestores no processo de atribuição de pacientes em leitos adequados ao seu tratamento. Através da implementação de um protótipo baseado neste modelo híbrido de apoio à decisão, nomeado de IMBEDS, foram avaliados 50 pacientes em um total de 266 leitos gerenciados pela Central de Leitos, no Hospital Mãe de Deus, localizado em Porto Alegre. O resultado final dos testes foi de 93,5% de similaridade entre o leito apto apresentado pelo modelo e o processo real de alocação dos enfermos. / The Bed Management is an important area of planning and control hospital. It’s function is to ensure the balance between the patients who come through the emergency department, elective that have some scheduled treatment and those leaving the hospital. Thus, the Bed Management enables the hospital keep high occupancy rate of rooms, but without fill all the beds, in addition to providing any unplanned situation. Effective management of hospital beds as a resource has always been a challenge for managers. In the 80s and 90s, for example, thousands of patients have operations canceled due to non-medical reasons. As there is need for better control of the flow, Bed Management area then began to receive more academic attention and also policies national for the Bed Management. The process of admission and positioning the patients, from the management of beds, has been developing in recent years through of operational research, such as simulation, queuing theory, statistical analysis, among others. Due to the uncertainties experienced by hospitals nowadays, the use of model Situation Awareness in research in the health field is growing increasingly. Situation Awareness is a field of study that seeks to understand the context of the environment and designing future actions. In short, it is a technique that goes beyond the traditional information processing, as it seeks to explain human behavior in the operation of complex systems. In this statement, this work aims to use the Situation Awareness in Bed Management area, using a hybrid model that combines the technique Artificial Neural Network Multilayer Perceptron with the Multi-Attribute Value Theory for decision making, assisting managers in process of patient's allocation to the bed suitable in his treatment. Through the implementation of a prototype based on this hybrid model of decision support, named of IMBEDS, were evaluated 50 patients in a total of 266 beds managed by Beds Center, in the Hospital Mãe de Deus, located in Porto Alegre. The final result of the tests was 93.5% similarity between the bed apt selected by the model and the allocation process of the patients.

Problems in the public policy decision-making environments are typically complex and continuously evolve. In a resource-constrained environment, several alternatives, criteria, and conflicting objectives must be considered. As a result, solutions to these types of problems cannot be modelled solely using single-criteria techniques. It has been observed that most techniques used to shape energy policy and planning either produce sub-optimal solutions or use strong assumptions about the preferences of decision-maker(s). This difficulty creates a compelling need to develop novel techniques that can handle several alternatives, multiple criteria and conflicting objectives to support public sector decision-making processes. First, the study presents a novel scenario-based multi-objective optimisation framework based on the augmented Chebychev goal programming (GP) technique linked to a value function for analysing a decision environment underlying energy choice among low-income households in isolated rural areas and informal urban settlements in South Africa. The framework developed includes a multi-objective optimisation technique that produced an approximation of a Pareto front linked to an a priori aggregation function and a value function to select the best alternatives. Second, the study used this model to demonstrate the benefits of applying the framework to a previously unknown subject in public policy: a dynamic multi-technology decision problem under uncertainty involving multiple stakeholders and conflicting objectives. The results obtained suggest that while it is cost-optimal to pursue electrification in conjunction with other short-term augmentation solutions to meet South Africa's universal electrification target, sustainable energy access rates among low-income households can be achieved by increasing the share of clean energy generation technologies in the energy mix. This study, therefore, challenges the South African government's position on pro-poor energy policies and an emphasis on grid-based electrification to increase energy access. Instead, the study calls for a portfolio-based intervention. The study advances interventions based on micro-grid electrification made up of solar photovoltaics (PV), solar with storage, combined cycle gas turbine (CCGT) and wind technologies combined with either bioethanol fuel or liquid petroleum gas (LPG). The study has demonstrated that the framework developed can benefit public sector decision-makers in providing a balanced regime of technical, financial, social, environmental, public health, political and economic aspects in the decision-making process for planning energy supply interventions for low-income households. The framework can be adapted to a wide range of energy access combinatorial problems and in countries grappling with similar energy access challenges.