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PurposeRisk assessment is a very important step toward managing risks in various organizations and industries. One of the most extensively applied risk assessment techniques is failure mode and effects analysis (FMEA). In this paper, a novel fuzzy multiple-criteria decision-making (MCDM)-based FMEA model is proposed for assessing the risks of different failure modes more accurately.Design/methodology/approachIn this model, the weight of each failure mode is considered instead of risk priority number (RPN). Additionally, three criteria of time, cost and profit are added to the three previous risk factors of occurrence (O), severity (S) and detection (D). Furthermore, the weights of the mentioned criteria and the priority weights of the decision-makers calculated by modified fuzzy AHP and fuzzy weighted MULTIMOORA methods, respectively, are considered in the proposed model. A new ranking method of fuzzy numbers is also utilized in both proposed fuzzy MCDM methods.FindingsTo show the capability and usefulness of the suggested fuzzy MCDM-based FMEA model, Kerman Steel Industries Factory is considered as a case study. Moreover, a sensitivity analysis is conducted for validating the achieved results. Findings indicate that the proposed model is a beneficial and applicable tool for risk assessment.Originality/valueTo the best of authors’ knowledge, no research has considered the weights of failure modes, the weights of risk factors and the priority weights of decision-makers simultaneously in the FMEA method.

Today, attention to the issue of industrial ecology in the world has become very high and significant, but in Iran this attention does not exist and is only a slogan and preliminary studies. The purpose of this study is to investigate these conditions and provide a structure and model for the implementation of industrial ecology in Iran. Industrial estates are one of the major centers of producing energy and industrial waste in the country. Developing Industrial Ecology (IE) faces significant obstacles. This study evaluates the barriers due to a combination model, including the DPSIR and Fuzzy Analytic Hierarchy Process (FAHP). It prioritizes the responses and alternatives regarding the driving forces, pressures, states, and impacts (criteria). In this study, different responses for IE development in Iran were evaluated and prioritized based on different indicators classified based on DPSIR and FAHP techniques and models. The results suggest prioritizing future IE development in Iran based on current infrastructure and conditions. To compare the analysis of two fuzzy numbers, a new division method based on a comparative example is used; to confirm the proposed method, a comparative example is described and presented. According to the field studies and research background of the research, the number of "18 criteria" and "four responses" were suggested. Finally, improving IE legal is the first step of modifying the industries clustering system, establishing IE national database and industries training. According to the identified priorities, practical suggestions for the development of Iran's industrial environmental model within the framework of legal requirements, infrastructure and regulations that should be developed or updated, were presented and the duties of relevant legal agencies were identified and proposed.

Clustering is one of the most significant research area in the field of data mining and considered as an important tool in the fast developing information explosion era.Clustering systems are used more and more often in text mining, especially in analyzing texts and to extracting knowledge they contain. Data are grouped into clusters in such a way that the data of the same group are similar and those in other groups are dissimilar. It aims to minimizing intra-class similarity and maximizing inter-class dissimilarity. Clustering is useful to obtain interesting patterns and structures from a large set of data. It can be applied in many areas, namely, DNA analysis, marketing studies, web documents, and classification. This paper aims to study and compare three text documents clustering, namely, k-means, k-medoids, and SOM through F-measure.