## 2 Literature Review  
As discussed previously, the most important aim of this study was to examine the practical effectiveness of AHP-GTMA, by reference to the results obtained using AHP-TOPSIS upon solving the same real problem, using the proposed multi-measure, rank-based comparative research flow. Thus, in this section, first, the main method to be benchmarked (i.e., AHP-GTMA as well as its basis, the GTMA approach), the rival approach that used (i.e., AHP-TOPSIS as well as its basis, the TOPSIS approach) and their common former stage (i.e., AHP) are reviewed (and in a reversed order for clarity). Current application literature reveals that not only TOPSIS but also the hybrid AHP-TOPSIS are popular methods for MADM. In contrast, relatively fewer studies have applied the more recent AHP-GTMA approach; even the applications of GTMA are increasing now. Following this systematic review, it is observed that: (1) using AHP-TOPSIS as the rival approach is appropriate because the benchmarked AHP-GTMA approach is compared to a \"widely used approach\" [7]; and (2) it is suitable to benchmark the effectiveness of AHP-GTMA in practice at this moment because more upcoming applications of which is expected (similar to the increasing popularity of applying pure GTMA) but confidence in its application is still insufficient. As for the mentioned problem-data-model fitness of AHP-TOPSIS and the novelty of the proposed recursive algorithm for implementing AHP-GTMA in R, these are relatively minor for the study and are presented below along with the modeling and model implementation works. AHP is a widely used MADM approach that was proposed in 1977 [8]. It has been used in many recent application studies (e.g., [9; 10; 11; 12; 13; 14; 15]). AHP uses the concept of pairwise comparisons during investigation to prioritize or rank the criteria and/or the alternatives during calculation. With standard AHP, the involved criteria with respect to the total decision goal are pairwise compared to determine the criteria weight vector (CWV) and to have a priority over them. This is usually called the first phase. The involved alternatives are then pairwise compared with respect to (each of) these criteria to determine the preferred priority sequence over them. For AHP, there is also evidential cross-method integration [16; 17; 18; 19]. To the authors' knowledge, it is still the most popular MADM method for field applications. For example, in the supply chain field, until this decade, except for the MODM models, which are usually based on mathematical programming, AHP is the top-rated MADM approach for tackling with the supplier selection problems [20; 21] and this is also true in a survey for green supplier selection methods in 2015 [22]. Based on its popularity (i.e., the application domain) and since AHP has been well developed over half a century (i.e., the methodological domain), it is used as the shared initial phase for the two methods that are to be compared. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) was proposed at the end of the last century [5; 23]. In the MADM school of MCDM, it gained popularity quickly. TOPSIS utilizes the concept of relative closeness to the ideal, while \"relative closeness\" of an alternative is measured by the ratio of distance to the anti-ideal solution of the model (system) against the summed distance to both the ideal and anti-ideal (solutions). The greater this ratio is, the closer the alternative is to the ideal solution and the farther it is from the anti-ideal solution. Even after 2010, studies using the non-hybrid versions of TOPSIS have been undertaken [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. Similar to AHP, these TOPSIS studies can be roughly categorized as methodological improvement studies and model application studies. In those methodological improvement studies, we found that many studies since 2000 have used AHP-TOPSIS [36; 37; 38; 39; 40].