## 2 Literature Review
### Fuzzy Theory in Supplier Selection  
Decisions can be described as the final outcome of certain psychological and reasoning processes. They are often based on the judgment of experts and/or the values of stakeholders. Decisions may be influenced by subjectivity and uncertainty, and experts therefore tend to rely on their own standards. However, this expert subjectivity constitutes the main flaw in their decision-making process. [PERSON] [35] stated that human thinking and decision-making processes often include impressions, emotions, and intuition. Therefore, as long as there are people involved, uncertainty and ambiguity will be present. This means that traditional two-value logic rarely applies in practice. To solve this problem, [PERSON] [8] presented a fuzzy set and the concept of membership grades. Thereafter, various researchers have tackled the supplier evaluation and selection problem in fuzzy environments. [PERSON] and [PERSON] [36] presented an integrated fuzzy MCDM method using quality function deployment (QFD) to create a two-tuple linguistic representation model to assess supplier selection in a private hospital in Istanbul. [PERSON] and [PERSON] [20] utilized a multi-criteria approach based on fuzzy QFD to screen and select the best supplier for a company in the automotive industry. [PERSON] and [PERSON] [37] applied the fuzzy analytic network process (ANP) and grey ViseKriterijumska optimizacija i Kompromisno Resenje (VIKOR in Serbian, which means multi-criteria optimization and compromise solution in English) techniques to choose the optimal supplier for firms in the wood and paper industry. [PERSON] et al. [38] introduced a fuzzy group decision-making method using TOPSIS, VIKOR, and grey relational analysis (GRA) methods to assess green supplier selection for the agri-food industry. [PERSON] et al. [39] used the six sigma quality indices (SSQIs) to develop a fuzzy green supplier selection model for the performance measurement of thin-film-transistor liquid-crystal display (TFT-LCD) panel manufacturers. [PERSON] et al. [40] developed an integrated fuzzy grey TOPSIS method for supplier assessment and selection for a collaborative manufacturing company. [PERSON] et al. [41] utilized an integrated fuzzy AHP with multi-attributive border approximation area comparison (MABAC), weighted aggregated sum-product assessment (WASPAS), and TOPSIS to find the most suitable green suppliers for the automotive industry. In many real-world situations, fuzzy linguistic data are often included in a supplier selection environment since human judgement is usually vague and the decision-making context involves complexity and uncertainty. Under such circumstances, linguistic labels are more appropriate than quantitative values for the description of expert preferences or evaluations. In fuzzy theory, linguistic labels can be expressed as fuzzy numbers using conversion scales. By converting linguistic labels into fuzzy numbers, the fuzzy concepts of \"Good\" and \"Bad\" can be converted into clear and computable values. For example, the linguistic labels \"Very Good (VG)\", \"Good (G)\", \"Normal (N)\", \"Poor (P)\", and \"Very Poor (VP)\" can be converted to the following triangular fuzzy numbers: \"VG (7, 9, 10)\", \"G (5, 7, 9)\", \"N (3, 5, 7)\", \"P (1, 3, 5)\", and \"VP (0, 1, 3)\". Table 2 shows these linguistic labels and their corresponding fuzzy numbers [42]. The fuzzy numbers can then be converted into an integrated crisp value through defuzzification. Although these approaches are helpful, supplier selection involves knowledge from multiple fields, and cognitive differences and information loss may take place when the opinions of decision-makers are combined (that is, how much variation or dispersion is in the data set). From a statistical perspective, data with an overly-high standard deviation may indicate the existence of confusion or uncertainty