Yes / Failure Modes and Effect Analysis (FMEA) is a proactive, highly structured, and systematic approach for failure analysis. It has been also applied as a risk assessment tool, by ranking potential risks based on the estimation of Risk Priority Numbers (RPNs). This paper develops an improved FMEA methodology for strategic risk analysis. The proposed approach combines the Analytic Hierarchy Process (AHP) technique with the Exponential and Weighted Geometric Mean method (EWGM) to support risk analysis. AHP is applied to estimate the weights of three risk factors: Severity (S), Occurrence (O) and Detection (D), which integrate the RPN for each risk. The EWGM method is applied for ranking RPNs. Combining AHP with EWGM allows avoiding repetition of FMEA results. The results of the developed methodology reveal that duplication of RPNs has been decreased, and facilitating an effective risk ranking by offering a unique value for each risk. The proposed methodology focuses not only on high severity values for risk ranking but also it considers other risk factors (O and D), resulting in an enhanced risk assessment process. Furthermore, the weights of the three risk factors are considered. In this way, the developed methodology offers unique value for each risk in a simple way which makes the risk assessment results more accurate. This methodology provides a practical and systematic approach to support decision-makers in assessing and ranking risks that could affect long-term strategy implementation. The methodology was validated through the case study of a power plant in the Middle East, assessing 84 risks within 9 risk categories. The case study revealed that top management should pay more attention to key risks associated with electricity price, gas emissions, lost-time injuries, bad odor, and production. / This research has been supported by Hashemite University, Jordan.

Yes / Risk Management is one of the most relevant approaches and systematic application of strategies, procedures and practices management that have been introduced in literature to identifying and analysing risks which exist through the whole life of a product or a process. As a quality management tool, the novelty of this paper suggests a modified Failure Modes and Effect Analysis (FMEA) for understanding the non-technical risk comprehensively, and to attain a systemic methodology by decomposing the risk for nine risk categories including an appropriate 84 Risk Indicators (RI's) within all those categories through the Life Cycle (LC) stages of power plants. These risk categories have been identified as: economic risks, environmental and safety health risks, social risks, technological risks, customer/demand risks, supply chain risks, internal and operational business process risks, human resources risks and management risks. These indicators are collected from literatures. The enhanced FMEA has combined the exponential and the weighted geometric mean (WGM) to calculate the Exponential Weighted Geometric Mean-RPN (EWGM-RPN). The EWGM-RPN can be used to evaluate the risk level, after which the high-risk areas can be determined. Subsequently, effective actions either preventive or corrective can be taken in time to reduce the risk to an acceptable level. However, in this paper the FMEA will not adapt an action plan. Due to that, all RPN's will be considered depending on the point scale (1 to 5) afterward, the results will be combined and extended later with AHP. This developed methodology is able to boost effective decision- making about risks, improve the awareness towards the risk management at power plants, and assist the top management to have an acceptable and preferable understanding of the organisation than lower level managers do who are close to the day-to-day (tactical plan). Additionally, this will support the organisation to develop strategic plans which are for long term. And the essential part of applying this methodology is the economic benefit. Also, this paper includes developed sustainability perspective indicators with a new fourth pillar, which is the technological dimension. The results of the analysis show that the potential strategic makers should pay special attention to the environmental and internal and operational business process risks. The developed methodology will be applied and validated for different power plants in the Middle East. An expanded validation is required to completely prove drawbacks and benefits after completing the Analytical Hierarchy Process (AHP) model. / Hashemite University, Jordan

Yes / Risk Management is one of the most relevant approaches and systematic applications of strategies, procedures and practices management that have been introduced in literatures for identifying and analysing risks which exist through the whole life of a product ,a process or services. Therefore, the aim of this paper is to propose a risk assessment model that will be implemented to the energy sector, particularly to power plants. This model combines the Analytic Hierarchy Process (AHP) technique with a new enhanced Balance Score Card (BSC). AHP is constructed to determine the weights and the priorities for all perspectives and risk indicators that involved in the BSC. The novelty in this paper is not only in using the BSC for risk assessment, but also, in developing a new BSC with six perspectives, which are sustainability perspective; economic; learning and growth; internal and operational business process; supply chain and customer/demand perspective. Another three contributions of this paper are firstly, including the sustainability dimension in BSC, and covering nine risk categories, which comprise 84 risk indicators that have been distributed across the six risk BSC perspectives. Secondly, assessing the non-technical risks in power plants and finally, this research will concentrate on the strategic level instead of the operational level where the majority of researches focus on latter but the former is far less researched. The created model will provide an effective measurement for the risks particularly, in the power plants sector. The results of this study demonstrate that the supply chain risks perspective is the keystone for the decision making process. Furthermore, these risk indicators with the new structure of BSC with six perspectives, help in achieving the organisation mission and vision in addition to affording a robust risk assessment model. The inputs of this model are composed from a previous stage using a modified Failure Mode and Effect Analysis (FMEA) (which has been used the Exponential Weighted Geometric Mean (EWGM)) to understand and analyse all risks, after which, the results of the developed FMEA which are the Risk Priority Numbers (RPN’s), have been used to build the AHP-BSC risk model. These risks are collected with difficulty from various literatures. This study will be validated in the next stage in power plants in the Middle East. / Hashemite University, Jordan

Submitted by Sáboia Nágila (nagila.saboia01@gmail.com) on 2016-07-29T14:09:40Z No. of bitstreams: 1 Disertação-Anibrata Pal.pdf: 1139751 bytes, checksum: a29e1923e75e239365abac2dc74c7f40 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-08-15T17:54:46Z (GMT) No. of bitstreams: 1 Disertação-Anibrata Pal.pdf: 1139751 bytes, checksum: a29e1923e75e239365abac2dc74c7f40 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-08-15T17:57:29Z (GMT) No. of bitstreams: 1 Disertação-Anibrata Pal.pdf: 1139751 bytes, checksum: a29e1923e75e239365abac2dc74c7f40 (MD5) / Made available in DSpace on 2016-08-15T17:57:29Z (GMT). No. of bitstreams: 1 Disertação-Anibrata Pal.pdf: 1139751 bytes, checksum: a29e1923e75e239365abac2dc74c7f40 (MD5) Previous issue date: 2016-04-19 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The world of information retrieval revolves around web search engines. Text search engines are one of the most important source for routing information. The web search engines index huge volumes of data and handles billions of documents. The learn to rank methods have been adopted in the recent past to generate high quality answers for the search engines. The ultimate goal of these systems are to provide high quality results and, at the same time, reduce the computational time for query processing. Drawing direct correlation from the aforementioned fact; reading from smaller or compact indexes always accelerate data read or in other words, reduce computational time during query processing. In this thesis we study about using learning to rank method to not only produce high quality ranking of search results, but also to optimize another important aspect of search systems, the compression achieved in their indexes. We show that it is possible to achieve impressive gains in search engine index compression with virtually no loss in the final quality of results by using simple, yet effective, multi objective optimization techniques in the learning process. We also used basic pruning techniques to find out the impact of pruning in the compression of indexes. In our best approach, we were able to achieve more than 40% compression of the existing index, while keeping the quality of results at par with methods that disregard compression. / Máquinas de busca web para a web indexam grandes volumes de dados, lidando com coleções que muitas vezes são compostas por dezenas de bilhões de documentos. Métodos aprendizagem de máquina têm sido adotados para gerar as respostas de alta qualidade nesses sistemas e, mais recentemente, há métodos de aprendizagem de máquina propostos para a fusão de evidências durante o processo de indexação das bases de dados. Estes métodos servem então não somente para melhorar a qualidade de respostas em sistemas de busca, mas também para reduzir custos de processamento de consultas. O único método de fusão de evidências em tempo de indexação proposto na literatura tem como foco exclusivamente o aprendizado de funções de fusão de evidências que gerem bons resultados durante o processamento de consulta, buscando otimizar este único objetivo no processo de aprendizagem. O presente trabalho apresenta uma proposta onde utiliza-se o método de aprendizagem com múltiplos objetivos, visando otimizar, ao mesmo tempo, tanto a qualidade de respostas produzidas quando o grau de compressão do índice produzido pela fusão de rankings. Os resultados apresentados indicam que a adoção de um processo de aprendizagem com múltiplos objetivos permite que se obtenha melhora significativa na compressão dos índices produzidos sem que haja perda significativa na qualidade final do ranking produzido pelo sistema.