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A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods’ outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems.

Many managers and firm owners nowadays pay special attention to green supplier selection to gain competitive advantage all over the world. Consequently, this subject a critical and significant decision for firms. This study utilizes an extension to analytical hierarchy process (AHP) under interval type-2 fuzzy environment (IT2FAHP) model to better cope with ambiguity and vagueness for solving supplier selection problem considering green concepts. A real case application is also performed in a home appliance manufacturer to demonstrate the effectiveness and efficiency of the IT2FAHP model in the present paper. The findings indicate that the most important factors that are effective in selecting green suppliers are cleaner production, energy/material saving, green package, remanufacturing, and environmental management system. Further, a comparison and a sensitivity analysis are conducted to display the consistency and stability of the proposed model. The findings reveal that the IT2FAHP model is quite consistent with the models in literature and green product, cleaner production, green design, and green package have a significant positive effect on performances of green suppliers.

The aim of this study is not only to give self-contained and methodological steps of data mining with its areas of applications, but also to provide a compact source of reference for the researchers who want to use data mining and fuzzy inference in their area of work. We construct a fuzzy inference system to predict the profit of the major 500 industrial enterprises of Turkey. For this aim, we use most of the data mining tools. First, we use fuzzy $$c$$ c -means clustering algorithm and obtain the linguistic terms of the variables. Having used decision tree technique, fuzzy rules are revealed. Eventually, we compare various defuzzification strategies to obtain crisp prediction values of our fuzzy inference system. We can conclude that the prediction results of the smallest of maxima defuzzification strategy-based fuzzy inference system has circa 40 % smaller sum square error than that of classical regression model.