## 2 Related Works  
Multiple-criteria decision making (MCDM) encompasses a variety of methods that support decision makers in evaluating and choosing the most suitable decision from a set of alternatives, based on multiple criteria. Over the years, many approaches have been developed and proposed for MCDM problems, as presented in Table 1. A closer look at the overview reveals that primitive methods tend to be used in an integrated manner [5]. Most combinations of MCDM methods aim to individually perform the two tasks of determining the importance of the criteria and prioritizing alternatives [6]. For example, [PERSON] et al. combined two MCDM methods--the Best-Worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)--for the purpose of supplier selection [7]. In that study, the criterion weight was calculated using the BWM method; then, the ranking of risks affecting the supplier were calculated through the TOPSIS method. [PERSON] et al. combined two MCDM methods as a combined AHP-VIKOR method for the purpose of evaluating the sustainable performance of face-shield frame production [8]. A new era in MCDM began when [PERSON] introduced the concept of fuzzy sets [9]. Fuzzy set theory has been proven to be powerful in dealing with various MCDM problems to overcome uncertainty, inaccurate data, and unclear information. Fuzzy MCDM has been used in a wide range of practical applications. For example, [PERSON] analyzed environmental regulation and green economic efficiency in China by applying the fuzzy analytical hierarchy process (AHP) andthe fuzzy VleKriterijumska Optimizacija I Kompromison Resenje (VIKOR, a Serbian term for \"multi-criteria optimization and compromise solution\") [10]. [PERSON] et al. evaluated the quality of electronic services in the airline industry from the consumer's point of view, using an integrated fuzzy analytical hierarchy process (F-AHP) and fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution (F-MARCOS) approach [11]. [PERSON] et al. introduced an integrated approach--TODIM (an acronym in Portuguese for interactive and multiple-attribute decision making)-ELECTER II (elimination and choice translating reality II)--to solve technology selection problems [12]. In addition, extensions with fuzzy sets of the MCDM method are being introduced with increasing popularity. Therefore, the development of fuzzy set types nearly parallels the development of MCDM methods. The most recent fuzzy development is the introduction of spherical fuzzy extensions of MCDM methods [13; 14; 15]. Recent studies show that evaluation based on distance from average solution (EDAS) is emerging as one of the most effective assessment methods. This distance-based method was introduced by [PERSON] et al. in 2015 [16]. The EDAS method is a multi-criteria distance-based decision-making methods similar to TOPSIS and VIKOR. However, the EDAS method simplifies the distance calculation to speed up the decision-making process [17]. EDAS also outperforms TOPSIS and VIKOR in terms of complexity, due to the elimination of unpromising candidates. In addition, this method is very convenient when there is information about the preferred mean of the attribute evaluation [18]. Another popular MCDM method to identify potential relationships between factors and weighting factors for the evaluation process is the decision-making trial and evaluation laboratory (DEMATEL) method. This matrix-based method was first introduced in 1974 to solve complex and interdependent problems in various fields, such as manufacturing, supply chain technology, and services [19; 20; 21]. As discussed above, fuzzy theory is continuously developed and applied to MCDM methods to deal with ambiguities in human perspective. Among these applications, spherical fuzzy is one of the recently developed and introduced fuzzy sets. The spherical fuzzy set is capable of expressing membership, non-membership and hesitation in decision makers' opinions.