Submitted by Nadir Basilio (nadirsb@uninove.br) on 2016-06-01T14:36:20Z No. of bitstreams: 1 Jose Luiz Rodrigues Junior.pdf: 1816384 bytes, checksum: f83b0cfa9558fadd55f06d3f851584a9 (MD5) / Made available in DSpace on 2016-06-01T14:36:20Z (GMT). No. of bitstreams: 1 Jose Luiz Rodrigues Junior.pdf: 1816384 bytes, checksum: f83b0cfa9558fadd55f06d3f851584a9 (MD5) Previous issue date: 2015-06-17 / In current data centers, processing routines are scheduled to perform in a given period of time and, in case of delay, make it impossible to conduct the activities of everyday life of the various sectors of the company. Even with modern servers, due to the large volume of data to be processed, it is possible that certain computers are overloaded causing a drop in performance, a longer time to implement and consequently delays in the delivery of results. In this case, alternative scenarios can be created to sequence the tasks again. This work aimed to apply the multi-criteria decision analysis method on the results of the simulation of alternative scenarios for sequencing tasks on computers in a datacenter, in order to overload the resolution of problems. To achieve this purpose have been developed simulation models of task sequencing scenarios and replacement of computer overloaded. The analysis of simulation results was made based on three criteria, namely the Total Processing Time (makespan), the Total delay time, and the number of Delayed Tasks. Subsequently, the results were submitted to analysis method of AHP multicriteria decision (Analitycal Hierarchy Process), allowing you to choose the best setting according to established criteria. It was concluded that the modeling and computer simulation, combined with multi-criteria decision analysis method, can aid in decision making, as it enables sequencing to identify the best solution possible alternative scenarios in front of a computer overload situations. / Nos atuais Datacenters, as rotinas de processamento são programadas para executar em determinado período de tempo e, em caso de atraso, impossibilitar a condução das atividades do dia a dia dos diversos setores da empresa. Mesmo com os modernos servidores, devido ao grande volume de dados para serem processados, é possível que determinados computadores sejam sobrecarregados provocando uma queda em seu desempenho, um tempo maior para a execução e, consequentemente, atrasos na entrega dos resultados. Nesse caso, cenários alternativos podem ser criados para sequenciar novamente as tarefas. Assim, este trabalho teve por objetivo aplicar o método de análise de decisão multicritério nos resultados da simulação de cenários alternativos para o sequenciamento de tarefas em computadores de um Datacenter, visando a resolução para problemas de sobrecarga. Para atingir esse propósito, foram desenvolvidos modelos de simulação de cenários de sequenciamento de tarefas e substituição do computador com sobrecarga. A análise dos resultados da simulação foi realizada considerando três critérios, a saber: o Tempo Total de Processamento (makespan), o Tempo Total de Atraso, e o Número de Tarefas Atrasadas. Posteriormente, os resultados foram submetidos ao método de análise de decisão multicritério AHP (Analitycal Hierarchy Process), permitindo escolher o melhor cenário segundo os critérios estabelecidos. Concluiu-se que a modelagem e simulação computacional, aliada ao método de análise de decisão multicritério, pode auxiliar na tomada de decisão, uma vez que possibilita identificar a melhor solução de sequenciamento em cenários alternativos possíveis diante de situações de sobrecarga de um computador.

This thesis holistically examines the potential for tidal stream turbine (TST) integration to displace diesel generated electricity in remote coastal First Nations communities within the Marine Plan Partnership for the North Pacific Coast region of British Columbia. This thesis utilizes a combination of spatial analysis (GIS Multi-Criteria Decision Analysis) to identify sites; stakeholder engagement to assess TST suitability, bridge knowledge gaps, and understand desired characteristics of community energy systems; and Levelized Cost of Energy (LCOE) analyses for existing diesel and externality included scenarios along with potential TST costs in a candidate community. Results illustrate the need for information within these communities, from resource quantification to characteristics of renewable energy technologies and system feasibility; self-sufficiency as being the primary transition driver; and funding/human resource capacity as being substantial barriers. Within the study region ≈89.8 km2 of feasible resource was identified, with ≈22 km2 of potentially suitable tidal resource in proximity to nine communities. The COVID-19 pandemic resulted in difficulties contacting and arranging interviews with the most suitable communities. Driven by the holistic research mandate requiring community stakeholder engagement to occur in tandem with the economic analyses, Queen Charlotte Village and Skidegate Landing on Haida Gwaii were chosen as the candidate communities, despite not being the most suitable identified communities. The community interviews revealed TSTs as being an acceptable renewable energy technology. Furthermore, the identified site in Skidegate Inlet (SI) was found to have favourable Marine Spatial Planning (MSP) for TST development. Existing diesel generation carries a LCOE of $0.63/kWh, being $0.08-0.14 more per kWh than the literature cited LCOE range for TSTs. The LCOE for CO2 equivalent externalities at current carbon tax prices was found to be an additional $0.02/kWh. Despite having a technically viable peak spring current speed, the SI site was financially unviable for 284 kW of rated capacity across all diesel LCOE scenarios driven by capacity factor (1.62%), high cabling costs (approximately one third of capital costs), and outdated data/assumptions within the Natural Resources Canada Tidal Project Cost Estimation tool used in the tidal LCOE calculations. This work contributes to the progression of tidal energy development on BCs coast along with demonstrating the utility of holistic assessment frameworks for RETs across environmental, social, and economic considerations. The results of this thesis can inform existing MSP efforts in the Marine Plan Partnership for the North Pacific region and the framework developed can be built upon and altered for global use in pursuit of sustainable energy transitions. / Graduate

This thesis analyses how public decision-makers can rapidly and sustainably decarbonise their regions, considering the challenge that promoting timely decarbonisation represents. The research was divided into three phases and developed using desk studies and interviews with Project Drawdown (PD) experts, Swedish public officials and researchers. In phase one, PD solutions showed a shortcoming in sustainable operationalisation of global decarbonisation solutions. It was found that they alone could not account for sustainability, and that there is dependency on the context of application and on decision-making processes. In phase two, it was found that sustainability could be embedded in decision-making for decarbonisation through decision support. In the Blekinge example case, several challenges were found, for which sustainable decision support was needed. To respond to this challenge, a Decision Support tool (DST) was created, based on multi-criteria decision analysis. This three-step generic tool provides a relevance assessment to prove the suitability of decarbonisation solutions in the given context, a sustainability assessment to address sustainability challenges, and a prioritisation step for strategic decision-making. The tool was validated to be useful by Swedish policymakers and by international experts. This DST has the potential to support policy-making around the world to decarbonise regions rapidly and sustainably.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第23591号 / 地環博第218号 / 新制||地環||42(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 宇佐美 誠, 教授 諸富 徹, 准教授 吉野 章 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DGAM

<p dir="ltr">Most road fatalities are caused by human error. To help mitigate this issue and enhance overall transportation safety, companies are turning to advanced driver assistance systems and autonomous vehicle development. Perception, a key module of these systems, mostly uses light detection and ranging (LiDAR) sensors and enables efficient obstacle detection and environment mapping. Extensive research on the use of LiDAR for autonomous driving has been documented in the literature. Yet still, several researchers and practitioners have advocated continued investigation of LiDAR placement alternatives. To address this research need, this thesis research begins with a comprehensive review of different sensor technologies – camera, radio detection and ranging, global positioning system, and inertial measurement units – and exploring their inherent strengths and limitations. Next, the thesis research developed a methodological multiple criteria framework and implemented it in the context of LiDAR placement optimization. Given the numerous criteria and placement alternatives associated with LiDAR placement, multi-criteria decision analysis (MCDA) was identified as an effective tool for LiDAR placement optimization. MCDA has been applied to some extent in decision-making regarding autonomous vehicle development. However, its application in LiDAR placement optimization remains unexplored. In evaluating the LiDAR placement alternatives, the research first established the placement alternatives and then developed a comprehensive yet diverse set of criteria – point density, blind spot regions, sensor cost, power consumption, sensor redundancy, ease of installation, and aesthetics. The data collection methods included the CARLA simulator, sensor datasheets, and questionnaire surveys. The relative importance among the evaluation criteria was established using weighting techniques such as respondent-assigned weighting, equal weighting, and randomly generated weighting. Then, to standardize the different measurement units, scaling was carried out using value functions developed for each criterion using data from the respondents. Finally, the weighted and scaled criteria measures were amalgamated to obtain the overall evaluation score for each alternative LiDAR placement design. This enabled the ranking of the placement designs and the identification of the best-performing and worst-performing designs. Hence, the optimization method used is the enumeration technique. The findings of this study serve as a reference for future similar efforts that seek to optimize LiDAR placements based on select criteria. Further, it is expected that the thesis’s framework will contribute to an enhanced understanding of the overall impact of LiDAR placement on autonomous vehicles, thus enabling the cost-effective design of their placement and, ultimately, improving AV operational outcomes, including traffic safety.</p>

Transport infrastructure investment decision making is typically based on a range of inputs such as social, environmental and economic factors. The benefit cost ratio (BCR), a measure of economic efficiency (“value for money”) determined through cost benefit analysis (CBA), is dependent on accurate estimates of the various option costs and net social benefits such as reductions in travel time, accidents, and vehicle operating costs. However, most evaluations are deterministic procedures using point estimates for the inputs and producing point estimates for the outputs. Transport planners have primarily focused on the cost risks and treat risk through sensitivity testing. Probabilistic risk analysis techniques are available which could provide more information about the statistical confidence of the economic evaluation outputs. This research project report investigated how risk and uncertainty are dealt with in the literature and guidelines. The treatment of uncertainty in the Nelson Arterial Traffic Study (ATS) was reviewed and an opportunity to apply risk analysis to develop probabilities of sea level rise impacting on the coastal road options was identified. A simplified transport model and economic evaluation case study based on the ATS was developed in Excel to enable the application of @RISK Monte Carlo simulation software. The simplifications mean that the results are not comparable with the ATS. Seven input variables and their likely distributions were defined for simulation based on the literature review. The simulation of seven variables, five worksheets, and 10,000 iterations takes about 30 seconds of computation time. The input variables in rank order of influence on the BCR were capital cost, car mode share, unit vehicle operating cost, basic employment forecast growth rate, and unit value of time cost. The deterministically derived BCR of 0.75 is associated with a 50% chance that the BCR will be less than 0.6, although this probability is partly based on some statistical parameters without an empirical basis. In practice, probability distribution fitting to appropriate datasets should be undertaken to better support probabilistic risk analysis conclusions. Probabilities for different confidence levels can be reported to suit the risk tolerance of the decision makers. It was determined that the risk analysis approach is feasible and can produce useful outputs, given a clear understanding of the data inputs and their associated distributions.

<p>U radu su istražene mogućnosti za razvoj modela za izbor<br />lokacije proizvodnih sistema. Razvijen je model za izbor<br />lokacije proizvodnih sistema i verifikovan u studiji<br />slučaja na teritoriji AP Vojvodine i opštine Inđija.<br />Posebno značajan rezultat je fazi ekspertni sistem za<br />analizu kriterijuma odlučivanja, geografski informacioni<br />sistem za skrining i prostorni sistem za podršku u izboru<br />lokacije proizvodnih sistema.</p> / <p>This paper examines the possibilities of model development for<br />production systems site selection. Developed model for production<br />systems site selection is presented and tested in case study of AP<br />Vojvodina and Indjija municipality. A particularly important result is<br />developed fuzzy expert system for factor analysis, geographic<br />information system for screening and spatial decision support system.</p>

There is broad consensus that a transition to renewable energy and a low-carbon economy is crucial for future development and prosperity, yet there are differing perspectives on how such a transition should be achieved. The overarching goal of this dissertation, which is comprised of three interrelated studies, is to analyze and compare energy futures scenarios to achieve a renewable energy transition and low-carbon economy in the State of Vermont. In the first study, an analysis is presented of the role of energy pricing regimes and economic policy in the context of pursuing a renewable energy transition in the State of Vermont. Through the development and application of a system dynamics model, results address the limits to technological substitution due to path dependence on nonrenewable energy. The role of complementary economic policy is also highlighted to shift from a goal of quantitative growth to qualitative development in order to decouple economic welfare from energy consumption. In the second study, an analysis is presented of the impact of modeled energy transition scenarios to address energy development and land use trade-offs. Simulations with a spatio-temporal land cover change model find that Vermont could achieve a complete transition to renewable electricity using in-state resources through developing between 11,000 and 100,000 hectares of land for solar and wind, or up to four percent of state land area, including some environmentally sensitive land. This approach highlights the need for integration of energy policy and land use planning in order to mitigate potential energy-land use conflict. In the final study, trade-offs between energy, economic, environmental, and social dimensions of Vermont's renewable energy transition are explored through the use of a multi-criteria decision analysis. Energy transition alternatives were designed to reveal trade-offs at the intersection of economic growth and carbon price policy. While there were no optimal pathways to achieving Vermont's energy transition, some energy transition alternatives achieve a more socially desirable balance of benefits and consequences. Navigating the trade-offs inherent in the ongoing energy transition will require an adaptive approach to policymaking that incorporates iterative planning, experimentation, and learning.

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.

O estudo de sistemas logísticos envolve a concatenação de elementos estratégicos e operacionais, comumente compondo sistemas com múltiplas facetas, objetivos antagônicos e grande número de alternativas. Nesse contexto, o presente trabalho discute a utilização de análise de decisão multicritério (MCDA), simulação de eventos discretos (SED) e otimização para simulação. A metodologia MCDA captura, mensura e pondera os objetivos e valores dos tomadores de decisão. Por sua vez, a SED representa o sistema estudado com alto nível de detalhamento, permitindo a avaliação de diversas configurações do sistema. Por fim, métodos de otimização para simulação possibilitam a busca e comparação de alternativas mais eficientes. As três metodologias são avaliadas, identificando suas vantagens, desvantagens e complementaridades quando aplicadas a sistemas logísticos. Através da aplicação de um estudo de caso sobre o dimensionamento de um sistema de transporte, constatou-se que: a) a SED incorporou detalhes importantes para a avaliação mais precisa de vários indicadores de desempenho b) a metodologia MCDA possibilitou a captura de vários objetivos e valores, propiciando a realização de tradeoffs robustos; c) um método de busca exaustiva e técnicas de redução de variância permitiram a comparação das alternativas em tempos computacionais reduzidos. Por fim, conclui-se que a metodologia híbrida apresentada expande o potencial de aplicação da SED em sistemas logísticos complexos. / A logistic system study involves strategic and operational elements, commonly composing multi-faceted systems with antagonistic goals and large number of alternatives. In this context, this thesis discusses the use of multi-criteria decision analysis (MCDA), discrete event simulation (DES) and optimization for simulation. The MCDA methodology captures, measures and weighs the goals and values of decision makers. DES is useful for representing systems with high level of detail, allowing the evaluation of several system configurations. Finally, optimization for simulation procedures are useful for searching and comparing more efficient alternatives. These three methodologies are assessed and their advantages, disadvantages, and complementarities are identified for logistics systems applications. Through a case study of a transportation system, we conclude that: a) the SED incorporated important details for more precise evaluation of various performance indicators b) the MCDA methodology was useful to capture several goals and values, so that robust tradeoffs could be carried out c) an exhaustive search routine and variance reduction techniques allowed the comparison of several alternatives in feasible computational times. Finally, we conclude that the presented hybrid methodology expands the application of DES to complex logistics systems.