ABSTRACT One of industrial companies’ challenges, especially for intensive-use plants and other assets, is the proper sizing of the stock of strategic spare parts, items that have a history of low consumption, but whose lack can cause delays in repair and maintenance services, at the extreme leading to operational shutdowns. Effects can be from small to large scales. While on one hand, having a large stock of strategic items can provide a greater guarantee of operational availability, on the other hand, it brings additional storage and preservation costs, in addition to fixed capital outlays. A compromise solution is needed. The use of traditional or simpler techniques to infer the ideal level of stock for each spare often suffers from lack of historical data, especially in installations in the initial phase of the operation and maintenance cycle. Another problem is the diversity of applications for some materials. The present work proposes a method based on reliability and Bayesian hierarchical models (HBMs) to overcome the problems of data scarcity, uncertainties and variability between applications of each spare part. The criticality of the equipment or assets in which the spare parts are applied is taken into account in the method. The hierarchical Bayesian model enables updating information as new consumption of strategic items is registered. The method is tested for a stationary offshore oil and gas unit. companies challenges intensiveuse intensive services shutdowns scales hand availability costs outlays needed cycle materials HBMs (HBMs scarcity part registered unit

ABSTRACT In the development of new designs for systems or improvement of existing ones, the search for effective methods of reliability allocation is fundamental to achieve the minimum requirements. The different allocation methods can involve the consideration of many criteria. Among these criteria, some are quantitative, and others are qualitative, but in both cases, there will be uncertainties associated with the metrics. This article presents an approach based on the probabilistic composition of preferences to allocate the reliability of subsystems considering the uncertainties associated with each criterion considered. The results obtained with application to a case described in the literature demonstrate the efficacy of the proposed approach to deal with this class of problems.

ABSTRACT This study associates graph theory and a multi-criteria decision aid technique, presenting a different process for doing the investigation of criminal networks. In the criminal subject, privacy concerns limit identification. For this reason, the database composed of 110 actors, involving criminals and peripheral characters to the network, was identified by numbers, without names and penalties. The discrimination of critical actors in criminal networks can help law enforcement officers to conduct a more detailed investigation for their disruption. Communication between drug traffickers was transformed into different centrality indices for each actor in their social network. Centralities and actors compose a decision matrix, analyzed by the Composition of Probabilistic Preferences to identify the most relevant actors in the criminal network. Results indicated that the five actors highlighted in the real investigation have a clear distinction of importance in the network, which in a way have been ratified.

RESUMO Esta pesquisa teve como objetivo auxiliar gestores e comissões técnicas na escolha de goleiros do futebol profissional. Foi estudada uma amostra de 64 goleiros que atuam na Argentina e no Brasil. Foram analisados seus desempenhos em jogos de duas temporadas, considerando três critérios: gols sofridos por minutos jogados, percentual de gols evitados e percentual de partidas sem sofrer gols. A Composição Probabilística de Preferências foi o método escolhido para a modelagem, por considerar a variabilidade aleatória dos dados do problema e do futebol, considerado um dos esportes mais imprevisíveis. A aplicação comparou a escolha baseada na análise dos dados com as últimas convocações de goleiros para as seleções desses países. Os goleiros selecionados corresponderam aos de melhor desempenho individual, confirmando o modelo.

ABSTRACT This research aims to assist managers and technical commissions to choose professional soccer goalkeepers. A sample of 64 goalkeepers playing in Argentina and Brazil was studied. Their performance in the matches of two seasons were analyzed considering three criteria: goals against per minute played, percentage of goals and percentage of matches without conceded goals. The Composition of Probabilistic Preferences (CPP) was the method chosen for modeling, considering the random variability in the problem data and in football, considered one of the most unpredictable sports. CPP allowed to compare the choice based on the data analysis to the latest goalkeeper call-ups for these countries’ national teams. The selected goalkeepers corresponded to those presenting the best individual performance, which confirms the model.

RESUMEN Esta investigación tuvo como objetivo ayudar a gerentes y comisiones técnicas para elegir a los porteros de fútbol profesional. Una muestra de 64 porteros de Argentina y Brasil fue estudiada. Las actuaciones en los partidos de dos temporadas fueran analizadas, considerando tres criterios: goles concedidos por minutos jugados, porcentaje de goles evitados y porcentaje de partidos en los que al portero no le encajaron goles. La Composición Probabilística de Preferencias fue el método elegido, considerando la variabilidad aleatoria de los datos del problema y del fútbol, considerado uno de los deportes más impredecibles. La aplicación comparó la muestra con las últimas convocatorias de porteros de las selecciones nacionales de estos países. Los porteros seleccionados correspondieron a los de mejor rendimiento individual, confirmando el modelo.

The main purpose of the present work is to present an application of the probabilisticpreferences composition method to the vendor and carrier selection process in the construction sector of the Southern Region of Rio de Janeiro state, in Brazil. The traditional measures associated with vendor and carrier evaluation are considered in the model and are combined through a preferences composition, which considers the options' uncertainties. In the application, we considered three different suppliers of sand for construction and three different logistics providers, two of them outsourced. Our intention was to verify if outsourcing can be considered a better solution, as proposed by many authors. The results show thatoutsourcing is the best option for different probabilistic composition points of view as well as when the overall evaluation is obtained by average composition.

In this paper we present a linear programming (LP) approach to risk prioritization in failure mode and effects analysis (FMEA). The LP is a data envelopment analysis (DEA)-based model considering weight restriction. In a FMEA, we commonly consider three criteria to prioritize the failure modes, occurrence, severity and detectability. These criteria are in an ordinal scale commonly varying from 1 to 10, higher the figure worse the result. Considering the values established for each criteria, in traditional FMEA one adopts a Risk Priority Number, calculated considering the product of criteria, which has been very criticized due to its shortcoming. Through the proposed approach a frontier is established considering the less critical failure modes. Considering this frontier, one can establish how much each failure mode must be improved to become relatively acceptable. A simplified case concerning an AFWS of a two loops PWR power plant is presented to shows the applicability of the proposed approach.

O presente trabalho apresenta uma abordagem baseada em análise envoltória de dados (Data Envelopment Analysis - DEA) para estabelecer direcionamentos de melhoria para os modos de falhas identificados na análise dos modos e efeitos de falha (FMEA). A abordagem tradicional da FMEA baseia-se num número de priorização de riscos que vem sendo alvo de inúmeras críticas embasadas em diversos artigos científicos. No presente trabalho, o número de priorização de risco é baseado em DEA. Por meio desse número de priorização de risco DEA, considerando-se o conceito de fronteira de eficiência, serão identificadas diretrizes de melhoria para os modos de falha com base nos índices de risco estabelecidos pela equipe executora da FMEA. Por meio dos resultados alcançados nas duas aplicações práticas, pode-se demonstrar a eficácia da abordagem proposta para tratar essa classe de problemas.

The present study presents a data envelopment analysis (DEA) based approach to establish guidelines for the improvement of the failure modes identified by a failure mode and effect analysis (FMEA). The traditional FMEA approach is based on risk priority number (RPN), which has been target of scientific criticism. In the present study, the RPN is based on DEA and considers the concept of efficiency frontier. Based on this concept and on the risk indexes, some improvement directives for the failure modes can be established. The obtained results concerning the two practical applications demonstrate the effectiveness of the proposed approach in dealing with this kind of problem.

O uso de processos estocásticos pontuais para modelar a confiabilidade de sistemas reparáveis tem sido constante para estabelecer medidas de sobrevivência em cenários de falha versus reparo. Entretanto, os processos tradicionais não consideram o real estado no qual um item retorna à condição operacional. O processo de renovação tradicional considera uma filosofia de "tão-bom-quanto-novo", enquanto um processo não-homogêneo de Poisson é baseado no conceito de reparo mínimo. Neste trabalho, é apresentada uma abordagem baseada no conceito de Processo de Renovação Generalizado (PRG), que é uma generalização de processo de renovação e de processo não-homogêneo de Poisson. Uma modelagem estocástica será apresentada para análise de disponibilidade de sistemas, incluindo planejamento de testes e/ou manutenção preventiva. Para validar a abordagem proposta, um estudo de caso foi desenvolvido para um hipotético sistema auxiliar de água de alimentação de uma usina nuclear, usando algoritmo genético como ferramenta de otimização.

The use of stochastic point processes to model the reliability of repairable systems has been a regular approach to establish survival measures in failure versus repair scenarios. However, the traditional processes do not consider the actual state in which an item returns to operational condition. The traditional renewal process considers an "as-good-as-new" philosophy, while a non-homogeneous Poisson process is based on the minimal repair concept. In this work, an approach based on the concept of Generalized Renewal Process (GRP) is presented, which is a generalization of the renewal process and the non-homogeneous Poisson process. A stochastic modeling is presented for systems availability analysis, including testing and/or preventive maintenances scheduling. To validate the proposed approach, it was performed a case study of a hypothetical auxiliary feed-water system of a nuclear power plant, using genetic algorithm as optimization tool.

Neste artigo apresenta-se a aplicação de um modelo híbrido, baseado na análise envoltória de dados e em conjuntos fuzzy, para estabelecer uma priorização dos modos de falha levantados numa análise dos modos e efeitos de falha. O objetivo principal é ilustrar a aplicabilidade da modelagem para analisar sistemas complexos, como o sistema de controle químico e volumétrico de uma usina nuclear do tipo água pressurizada, onde uma grande quantidade de modos de falha deve ser avaliada. O modelo apresentado possibilita tratar variáveis linguísticas, i.e., permite que se tratem as opiniões de especialistas de modo mais efetivo num contexto de análise de riscos industriais quando há poucos dados disponíveis, como pode ocorrer em projetos com inovação tecnológica. Os resultados obtidos demonstram o potencial e a viabilidade de se utilizar uma modelagem híbrida para se estabelecer uma ordem de prioridade entre os diferentes modos de falha associados a um sistema complexo.

In this paper one presents a data envelopment analysis and fuzzy sets based hybrid model application, in order to establish a priority ranking for the failure modes evaluated in a failure mode and effect analysis. The main objective is to present the applicability of this hybrid model to analyze complex systems, as the chemical and volumetric control system of a pressurized water reactor power plant, where a great bunch of failure modes must typically be evaluated. The presented modeling makes it possible the consideration of linguistic variables, i.e., it permits that one considers most effectively expert opinion in a risk analysis context when scarce data is available, as may occur in the plant design phase. The results obtained demonstrated the powerfulness and the viability of use of the proposed hybrid modeling to establish a priority ranking among many failure modes concerning a complex system.