The aim of this study is to propose a fuzzy multi-criteria decision-making approach (MCDM) to evaluate suppliers of one kind of medical device with respect to numerous criteria, simultaneously taking into account the type of each criteria and its relative importance. Two fuzzy MCDM methods are proposed for solving the treated problem. In the first step, the fuzzy pair-wise comparison matrix of the relative importance of criteria is constructed. The weight vector is calculated by using the developed procedure, and in the second step, the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is applied in order to rank possible suppliers of the considered kind of medical device. The rank is obtained using the process of fuzzy number comparison. A case study with real-life data is presented to verify the proposed method.

The ongoing research in the field of decision-making can be analyzed from different perspectives. Research trends indicate that multi-attribute decision-making (MADM) methods have a significant impact on engineering and management scientific areas. Since many of the problems existing in the mentioned areas are associated with a certain level of uncertainty, type 2 fuzzy sets represent a common solution for the enhancement of conventional MADM methods. In this way, the decision-makers are encouraged to use linguistic expressions for the assessment of attributes’ relative importance and their values. The purpose of this paper is to review a determination of attributes’ relative importance, and their values, as well as the extension of ranking methods with type 2 fuzzy sets. The papers are systematically adjoined to groups consisting of hybrid models with the following characteristics: (1) indicating the procedure for modeling attribute relative importance and their values, (2) determining the extension of MADM methods with type 2 fuzzy sets to determine attributes’ vector weights, and (3) the extension of MADM for attributes ranking with type 2 fuzzy sets. This study reviewed a total of 42 papers in the domain of engineering and management published from 2013 to 2023 in different journals indexed by the Springer, Science Direct, Emerald, Wiley, ProQuest, Taylor, and Francis research platforms.

The business environment is rapidly changing and puts pressure on enterprises to find effective ways to survive and develop. Since it is almost impossible to identify the multitude of complex conditions and business risks, an organization has to build its resilience in order to be able to overcome issues and achieve long term sustainability. This paper contributes by establishing a two-step model for assessment and enhancement of organizational resilience potential oriented towards Small and Medium Enterprises (SMEs) in the process industry. Using a dynamic modelling technique and statistical tools, a sample of 120 SMEs in Serbia has been developed as a testing base, and one randomly selected enterprise was used for model testing and verification. Uncertainties regarding the relative importance of organizational resilience potential factors (ORPFs) and their value at each level of business are described by pre-defined linguistic expressions. The calculation of the relative importance of ORPFs for each business level is stated as a fuzzy group decision making problem. First, the weighted ORPFs’ values and resilience potential at each business level are determined. In the second step, near optimal enhancement of ORPFs’ values is achieved by applying a genetic algorithm (GA).

The aim of this study was to propose a fuzzy decision-making model to evaluate customer satisfaction level which has a critical effect on the bank’s competitive advantage and its business. Value of customer satisfaction level can be determined by respecting all the factor values which are presented in this paper. The relative importance of indicators and their values are imprecise and uncertain variables which are modelled by using the fuzzy set theory. Relative importance of indicators under each identified customer satisfaction factor is stated by analogy to fuzzy analytic hierarchy process framework. Determining indicator values is stated as fuzzy group decision-making problem. The aggregated values of indicators are determined by using the fuzzy ordered weighted averaging operator. The overall index of customer satisfaction is determined by considering aggregated values of customer satisfaction factors. The level of customer satisfaction, which is closely linked with the quality of banking services, is determined by using fuzzy logic if–then rules. A case study with real-life data obtained from 22 enterprises is presented to verify the proposed method. It is shown that the proposed fuzzy model is a useful and effective assessment tool.

The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry.