## 1 Introduction
Professor [PERSON], who is an automatic control expert at the University of California USA, first developed the fuzzy theory [31]. [PERSON] made the fuzzy theory more mature by applying the fuzzy set in the probability theory in 1978. The fuzzy theory describes the fuzzy phenomenon in real life with precise mathematical language. As for the traditional questionnaire, respondents can only choose a number. If the respondent's feeling is an interval, they may answer distortedly and the researcher cannot gather the data correctly. In recent years, some researchers combined the ANP with the Fuzzy theory as FANP to solve this problem [32; 33; 34; 35; 36; 37; 38; 39]. However, [PERSON] holds a different perspective. He believes that Fuzzy AHP/ANP does not improve the outcome and should not be used because the intention of fuzzy perturbs the eigenvector in calculating processing by a small amount but not in a more valid direction [40]. In this research, in order to extract the consensus, the research method adopts FANP integrating with the similarity aggregation method (SAM) that is discussed below and de-fuzzification before the step of constructing pairwise comparison matrix. Once the FANP is applied, there are more methods to integrate the results of an assessment from every respondent of a question. Usually, the method of geometric mean is used to have the comprehensive opinion of respondents. Besides the geometric mean, if the research needs to extract the consensus part of the opinion, SAM can be used. [PERSON] et al. [41] proposed a method for measuring similarity in the fuzzy theory. [PERSON] and [PERSON] [42] cited and published the similarity aggregation method (SAM). The main concept is to use the intersection of two respondents' fuzzy evaluation to represent the degree of identity (consensus). As for the measurement of consensus, [PERSON] et al. proposed a model that generates a consistent decision in terms of the individual preferences and then measures the consensus that arises from it. The model reaches the consensus decision via the Borda and Copeland methods, and measures consensus via the [PERSON]'s measure. Different from the method of measuring the consensus of SAM, the [PERSON]'s measure calculates the agreement between the individuals' preferences and the final decision, but the SAM calculates the agreement between each expert [43]. Some research studies analyze the consensus opinion of respondents by SAM even though the method is much more complex than the geometric mean. [PERSON] et al. [44] researched the service quality criteria of long-term care institutions by integrating SERVQUAL, FAHP, and SAM. [PERSON] et al. [45] analyzed the selection of lubricant regeneration technology by FANP and SAM. As for the methods to integrate the results of the assessment, the geometric mean and SAM each have its own meaning, advantages, and disadvantages. Geometric mean is affected by extreme values but combines all expert opinions to represent a comprehensive opinion. SAM only extracts the intersection of expert opinions to represent the expert consensus and, thus, ignores the expert opinions that are far from reaching an agreement. Clearly, if more experts hold different opinions, the research results by FANP with geometric mean will be different from the result with SAM. Integrating opinions is an important key research step that affects the results of FANP. However, the research studies by FANP mentioned above integrated the results of assessment from every respondent only by one method. In recent years, some other methods of decision-making are proposed. [PERSON] et al. (2018) proposed three decision-making procedures for N-soft sets. The first two procedures rank the alternatives by giving the criteria weights and then calculate their extended choice values (ECVs) or extended weight choice values (EWCVs), respectively. The third procedure ranks the alternatives by giving the criteria weights and a threshold. These three methods are flexible because the user can give weights and the threshold individually.