International Journal of Industrial Engineering & Production Research

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Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-2 fuzzy clustering is the most preferred method. In recent years, neurology and neuroscience have been significantly advanced by imaging tools, which typically involve vast amount of data and many uncertainties. Therefore, Type-2 fuzzy clustering methods could process these images more efficient and could provide better performance. The focus of this paper is to segment the brain Magnetic Resonance Imaging (MRI) in to essential clusters based on Type-2 Possibilistic C-Mean (PCM) method. The results show that using Type-2 PCM method provides better results.

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Nowadays, with a growing body of features and technologies, supply chain management is being widely used to coordinate and optimize key processes such as increasing customer satisfaction, facilitating the processes, and enhancing product quality. In recent years, the emergence of IT and new business environments has led to the development of electronic supply chains. In order to use and benefit from the privileges of e-supply chains, organizations must identify the key factors in the implementation of e-supply chain management so that they can monitor the organization's current and future activities and take action to identify and modify and fix any bugs. The present study aimed at identifying these factors. Based on the available theoretical foundations and expert opinions, the factors affecting the implementation of electronic supply chain management were identified in seven factors with 31 indicators. To determine the weight of the identified factors considering the lack of independence between them, an integrated type-2 fuzzy AHP and type-2 fuzzy DEMATEL approach was used. Results showed that computer-based technology, infrastructure, inter-organizational relationships, and information are the most important factors.

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Preventive maintenance (PM) of machines has the critical role in a factory or enterprise. It decreases number of failures, increases reliability, as well as minimizes costs of production systems.  The managers’ duty of maintenance section is to prioritize machines and then, implement PM programs for them. Since machines have the different measures with respect to the maintenance costs, reliability, mean time between failures (MTBF), availability of spare parts, etc., the machines evaluation problem can be considered as a multiple criteria decision-making (MCDM) problem. Accordingly, the MCDM techniques can be applied to solve them. The aim of this paper is to extend the ELECTRE III (eLimination et choix traduisant la realite´– elimination and choice translation reality) method to interval type-2 fuzzy sets (IT2FSs) using curved (such as Gaussian) membership functions (MFs). The extended ELECTRE III methodology is then utilized to a maintenance group MCDM (GMCDM) matrix including the quantitative and qualitative criteria. In the proposed approach, the criteria weights, the assessment of alternatives with respect to criteria, and the thresholds are stated with Gaussian interval type-2 fuzzy sets (GIT2FSs). In order to show the effectiveness and applicability of the proposed approach, a case study and an illustrative example are exhibited using real decision-making problems. Due to the high correlation coefficients between our method and the others, as well as the results obtained by the proposed method, it can be taken into account as a valid and reliable approach to prioritize machines for PM.