A Produção Enxuta vem sendo aplicada em diversos setores, e a implantação dos seus princípios e práticas, elimina perdas nos processos e contribui para manter a competividade das empresas. Desse modo, o objetivo principal desta dissertação é desenvolver um modelo econômico-probabilístico que auxilie a seleção e priorização de projetos de produção enxuta. Este modelo quantifica os investimentos, benefícios e incertezas associadas através de uma análise econômica-probabilística que apresenta o retorno esperado dos projetos. Com base nesse retorno, a priorização dos projetos de produção enxuta torna-se mais precisa de ser executada pelos tomadores de decisão. O modelo contém 38 critérios qualitativos e quantitativos, os quais estão divididos em: (i) descrição do projeto, (ii) quantificação dos investimentos e (iii) quantificação dos benefícios. Para atingir o objetivo deste trabalho, foram necessárias cinco etapas: (i) selecionar os principiais critérios para avaliação de projetos de produção enxuta; (ii) escolher o método para seleção e priorização dos projetos de produção enxuta; (iii) estruturar um modelo genérico que avalie o retorno e o impacto das incertezas envolvidas no resultado dos projetos de produção enxuta, baseado nos critérios e método selecionados nas etapas anteriores; (iv) aplicar o modelo em uma empresa; (v) analisar e verificar os resultados da aplicação prática para validar o modelo desenvolvido. A principal contribuição desta dissertação é fornecer ao tomador de decisão um modelo que o auxilie a quantificar o retorno dos projetos de produção enxuta, combinando métodos econômicos e probabilísticos. Os métodos econômicos são de fácil entendimento e mais amigáveis aos tomadores de decisão, já os métodos probabilísticos avaliam as incertezas associadas aos projetos de produção enxuta, permitindo uma visão mais completa do retorno esperado. / Lean Manufacturing has been applied in several sectors and the implementation of its principles and practices eliminate losses in the processes, contributing to sustain companies’ competitiveness. Thus, this thesis main objective is to develop an economic-probabilistic model to aid lean manufacturing projects selection and prioritization. This model quantifies the investments, benefits and associated uncertainties based on an economic-probabilistic analysis, which presents the projects expected return. With the expected return, lean manufacturing projects prioritization becomes more precise to be performed by decision makers. The model contains 38 qualitative and quantitative criteria, divided into: (i) project description, (ii) investment quantification and (iii) benefits quantification. To achieve this study’s objective, five steps were performed: (i) selecting the main criteria for lean production projects evaluation; (ii) choosing the method for lean manufacturing projects selection and prioritization; (iii) developing a generic model to evaluate return and associated uncertainties impact on results of lean manufacturing projects, based on the criteria and method selected in previous steps; (iv) applying the model in a company; (v) analyzing and verifying the results from practical application to validate the developed model. The main contribution of this study is to provide to the decision maker a model to quantify lean manufacturing projects returns, connecting economic and probabilistic methods. Economic methods are easy to understand and more user-friendly to decision makers and probabilistic methods can evaluate associated uncertainties on lean manufacturing projects, allowing a more complete vision of the expected returns.

abstract: Biodiversity is required to guarantee proper ecosystem structure and function. However, increasing anthropogenic threats are causing biodiversity loss around the world at an unprecedented rate, in what has been deemed the sixth mass extinction. To counteract this crisis, conservationists seek to improve the methods used in the design and implementation of protected areas, which help mitigate the impacts of human activities on species. Marine mammals are ecosystem engineers and important indicator species of ocean and human wellbeing. They are also disproportionally less known and more threatened than terrestrial mammals. Therefore, surrogates of biodiversity must be used to maximize their representation in conservation planning. Some of the most effective surrogates of biodiversity known have only been tested in terrestrial systems. Here I test complementarity, rarity, and environmental diversity as potential surrogates of marine mammal representation at the global scale, and compare their performance against species richness, which is the most popular surrogate used to date. I also present the first map of marine mammal complementarity, and assess its relationship with environmental variables to determine if environmental factors could also be used as surrogates. Lastly, I determine the global complementarity-based hotspots of marine mammal biodiversity, and compare their distributions against current marine protected area coverage and exposure to global indices of human threats, to elucidate the effectiveness of current conservation efforts. Results show that complementarity, rarity, and environmental diversity are all efficient surrogates, as they outcompete species richness in maximizing marine mammal species representation when solving the minimum-set coverage problem. Results also show that sea surface temperature, density, and bathymetry are the top environmental variables most associated with complementarity of marine mammals. Finally, gap analyses show that marine mammals are overall poorly protected, yet moderately exposed to hotspots of cumulative human impacts. The wide distribution of marine mammals justify global studies like the ones here presented, to determine the best strategy for their protection. Overall, my findings show that less popular surrogates of biodiversity are more effective for marine mammals and should be considered in their management, and that the expansion of protected areas in their most important habitats should be prioritized. / Dissertation/Thesis / Doctoral Dissertation Biology 2019

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<p>Air pollution and a general concern for lack of physical activity in North America have motivated governments to encourage non-motorized modes of transportation. A key infrastructure component for these forms of transportation is sidewalks. The City of Saskatoon has identified the need to formalize sidewalk management policies to demonstrate diligence for community protection regarding sidewalk safety. Prioritization of sidewalk maintenance and rehabilitation actions must be objective and minimize risk to the community. Most research on prioritization of pedestrian facilities involved new construction projects. This research proposes a decision model that prioritizes a given list of existing unsafe sidewalk locations needing maintenance or rehabilitation using a direct measure of pedestrian safety, namely, quality-adjusted life years lost per year. </p><p>A decision model was developed for prioritizing a given list of unsafe sidewalk locations, aiding maintenance and rehabilitation decisions by providing the associated risk to pedestrian safety. The model used data mostly from high quality sources that had already been collected and validated. Probabilities and estimations were used to produce value-added decision policy.</p> <p>The decision analysis framework applied probability and multi-attribute utility theories. This study differed from other research due to the inclusion of age and gender groups. Total average daily population of the city was estimated. This population was distributed to sidewalk locations using probabilities for trip purposes and a locations ability to attract people relative to the city total. Then trip injury events were predicted. Age and gender distribution and trip injury type estimations were used to determine the impact of those injuries on quality of life.</p><p>There exist much observable high quality data that can be used as indicators of unknown or unobserved events. A decision policy was developed that prioritizes unsafe sidewalk locations based on the direct safety impact on pedestrians. Results showed that quality-adjusted life years lost per year sufficiently prioritized a given list of unsafe sidewalk locations. It was demonstrated that the use of conditional probabilities (n=594) allowed for the ability to abstract data representing a different source population to another. Average daily population confined and distributed within the city boundary minimized problems of accuracy. Gender-age distribution was important for differentiating the risk at unsafe sidewalk locations. Concepts from this research provide for possible extension to the development of sidewalk service levels and sidewalk priority maps and for risk assessment of other public services.</p>

<p>Air pollution and a general concern for lack of physical activity in North America have motivated governments to encourage non-motorized modes of transportation. A key infrastructure component for these forms of transportation is sidewalks. The City of Saskatoon has identified the need to formalize sidewalk management policies to demonstrate diligence for community protection regarding sidewalk safety. Prioritization of sidewalk maintenance and rehabilitation actions must be objective and minimize risk to the community. Most research on prioritization of pedestrian facilities involved new construction projects. This research proposes a decision model that prioritizes a given list of existing unsafe sidewalk locations needing maintenance or rehabilitation using a direct measure of pedestrian safety, namely, quality-adjusted life years lost per year. </p><p>A decision model was developed for prioritizing a given list of unsafe sidewalk locations, aiding maintenance and rehabilitation decisions by providing the associated risk to pedestrian safety. The model used data mostly from high quality sources that had already been collected and validated. Probabilities and estimations were used to produce value-added decision policy.</p> <p>The decision analysis framework applied probability and multi-attribute utility theories. This study differed from other research due to the inclusion of age and gender groups. Total average daily population of the city was estimated. This population was distributed to sidewalk locations using probabilities for trip purposes and a locations ability to attract people relative to the city total. Then trip injury events were predicted. Age and gender distribution and trip injury type estimations were used to determine the impact of those injuries on quality of life.</p><p>There exist much observable high quality data that can be used as indicators of unknown or unobserved events. A decision policy was developed that prioritizes unsafe sidewalk locations based on the direct safety impact on pedestrians. Results showed that quality-adjusted life years lost per year sufficiently prioritized a given list of unsafe sidewalk locations. It was demonstrated that the use of conditional probabilities (n=594) allowed for the ability to abstract data representing a different source population to another. Average daily population confined and distributed within the city boundary minimized problems of accuracy. Gender-age distribution was important for differentiating the risk at unsafe sidewalk locations. Concepts from this research provide for possible extension to the development of sidewalk service levels and sidewalk priority maps and for risk assessment of other public services.</p>

Single Nucleotide Polymorphisms (SNPs) are the most frequent genomic variations and the main basis for genetic differences among individuals and many diseases. As genotyping millions of SNPs at once is now possible with the microarrays and advanced sequencing technologies, SNPs are becoming more popular as genomic biomarkers. Like other high-throughput research techniques, genome wide association studies (GWAS) of SNPs usually hit a bottleneck after statistical analysis of significantly associated SNPs, as there is no standardized approach to prioritize SNPs or to select representative SNPs that show association with the conditions under study. In this study, a java based integrated system that makes use of major public databases to prioritize SNPs according to their biological relevance and statistical significance has been constructed. The Analytic Hierarchy Process, has been utilized for objective prioritization of SNPs and a new emerging methodology for second-wave analysis of genes and pathways related to disease associated SNPs based on a combined p-value approach is applied into the prioritization scheme. Using the subset of SNPs that is most representative of all SNPs associated with the diseases reduces the required computational power for analysis and decreases cost of following association and biomarker discovery studies. In addition to the proposed prioritization system, we have developed a novel feature selection method based on Simulated Annealing (SA) for representative SNP selection. The validity and accuracy of developed model has been tested on real life case control data set and produced biologically meaningful results. The integrated desktop application developed in our study will facilitate reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting timely identification of genomic disease biomarkers, and development of personalized medicine approaches and targeted drug discoveries.

One of the most important factors leading to success of a regional/national cleaner (sustainable) production strategy is sector-focused approach. Due to limited resources and other constraints, it is a necessity to make a prioritization between sectors for cleaner (sustainable) production practices. Thus, within the scope of this study, manufacturing industry sub-sectors in Izmir and in Turkey were prioritized based on various criteria. The results should assist policy makers in the preparation of related sectoral roadmaps and action plans.The prioritization of manufacturing industry sub-sector was accomplished via Multi- Criteria Decision Making (MCDM) method with the integration of recent available data and by taking feedback from the stakeholders. Investigation of the sectoral priorities was carried out both at regional (Izmir) and national (Turkey) level. The criteria used in prioritization of manufacturing industry sub-sectors in Izmir were water and energy consumption, amount of wastewater discharged, amount of solid waste and hazardous waste generated, greenhouse gas emissions, HerfindahlHirschman Index (statistical measure of market concentration), sectoral employment, number of companies, export share, added value and suitability for cleaner (sustainable) production. In the prioritization analysis of Turkey all of the aforementioned criteria for Izmir except Herfindahl-Hirschman Index, number of companies and added value were used.Based on the results of this study, the top five high priority industrial sectors for cleaner (sustainable) production practices in Izmir are basic metal industry, food products and beverages, chemicals and chemical products, other non-metallic mineral products and coke and refined petroleum. In the sectoral prioritization analysis for cleaner (sustainable) production in Turkey textile industry takes the place of coke and refined petroleum. These sectors coincide with the priority sectors identified based on different purposes by other regional and national institutions.

GWAS mainly aim to identify variations associated with certain phenotypes or diseases. Recently the combined p-value approach is described as the next step after GWAS to map the significant SNPs to genes and pathways to evaluate SNP-gene-disease associations. Major bottleneck of standard GWAS approaches is the prioritization of statistically significant results. The connection between statistical analysis and biological relevance should be established to understand the underlying molecular mechanisms of diseases. There are few tools offered for SNP prioritization but these are mainly based on user-defined subjective parameters, which are hard to standardize. Our group has recently developed a novel AHP based SNP prioritization algorithm. Beside statistical association AHP based SNP prioritization algorithm scores SNPs according to their biological relevance in terms of genomic location, functional consequence, evolutionary conservation, and gene-disease association. This allows researchers to evaluate the significantly associated SNPs quickly and objectively. Here, we have investigated the performance of the AHP based prioritization as the next step in the utilization of the algorithm in comparison to the other available tools for SNP prioritization. The user-defined parameters for AHP based prioritization have been investigated and our suggestion on how to use these parameters are presented. Additionally, the GWAS results from the analysis of two different sets of Alzheimer Disease Genotyping data with the newly proposed AHP based prioritization and the integrated software, METU-SNP, it was implemented, is reported and our new findings on the association of SNPs and genes with AD based on this analysis is discussed.

Transportation Asset Management (TAM) systems are in use at a significant number of transportation agencies. These systems can be used to effectively allocate resources and continuously inventory and monitor the condition of transportation infrastructure assets. Risk-oriented decision making is becoming an increasingly important component of the management process at many organizations, including transportation agencies. TAM systems can be used to incorporate risk assessment and risk management techniques at transportation agencies. To demonstrate the value of incorporating risk in TAM systems, an examination of the literature was performed, and a case study was conducted. This case study incorporated risk in bridge project prioritization through the utilization of data from the National Bridge Inventory (NBI), and application of Multi Attribute Decision Making (MADM) concepts to address uncertainty and prioritize selected bridges in the state of Georgia. The case study examines the impacts of data aggregation and disaggregation, and the incorporation of uncertainty on bridge project prioritization. Results of this analysis show that when available, disaggregate data on bridge condition should be used. In addition, uncertainty, in terms of performance risk, should be incorporated when past bridge condition data is available. Furthermore, decision-maker input is an important component of the Multi Attribute Utility Theory (MAUT) prioritization methodology used in this analysis. Decision-makers determine the relative importance of certain attributes, which is one of the strengths of this type of prioritization effort.

Healthcare facilities, including hospitals, are among the most challenging assets to maintain and modernize. An accurate performance assessment is essential for the appropriate prioritization of the subsystems that are competing for limited capital-renewal funds. Traditionally, physical condition has been the primary indicator of performance; however, other criteria have recently been added: level of service, sustainability, and risk, all of which are crucial for hospital buildings. This research introduces a practical and efficient framework for capital renewal for hospital facilities. The framework incorporates five unique aspects: (1) a two-dimensional hierarchy that accounts for the interrelationships between the hospital systems and the hospital spaces; (2) a multi-criteria performance assessment process that combines physical condition, level of service, sustainability, and risk of failure; (3) a visual all-on-site inspection application on hand-held tablet; (4) a mechanism for efficient prioritization of capital renewal tasks; and (5) optimization process for near-optimum allocation of capital-renewal of the limited capital renewal budget. The framework assesses hospital subsystems, incorporating consideration of the service quality within the indoor spaces and their impact on related subsystems. For renewal purposes, an appropriate subsystem priority index is then computed accordingly, taking into account the multi-criteria performance of the subsystems. Surveys of hospital maintenance experts have been used both for the collection of data for the development of the framework and for its validation. A prototype of the framework has been implemented in a user-friendly application whose performance was tested through two hospital case studies, the first of which was also employed for testing the prioritization and optimization functions of the framework. The results of six case study scenarios, with varying budget constraints and objective functions demonstrated the practicality and capability of the framework with respect to maximizing the performance of the facility relative to any desirable performance criteria. The proposed framework re-engineers the traditional process of facility performance assessment and also significantly enhances the capital renewal process by speeding the assessment process and efficiently allocating the renewal budget to maximize the return on the investment. This framework can be easily adapted to other types of building facilities and other infrastructure assets, thus contributing to sustaining the economy and the welfare of residents.