Rank-Based Comparative Research Flow Benchmarking the Effectiveness of AHP–GTMA on Aiding Decisions of Shredder Selection by Reference to AHP–TOPSIS

Abstract

The AHP–GTMA (analytic hierarchy process and graph theory and matrix approach) has been applied to select the best paper shredder before a company was making a bulk purchase order. However, there is a question as to whether one such relatively recent approach is effective to aid the selection decision problems in industrial/commercial practice. In this paper, a novel multi-measure, rank-based comparative research flow is proposed. The real decision problem case mentioned above is solved using the AHP–GTMA and the AHP–TOPSIS methods, respectively, with relevant datasets sourced. Several measures in the proposed flow, i.e., the arithmetical, geometrical, or even statistical ones, are multiplexed and used to validate the similarity between the rank order vectors (ROVs) (and thus between the final preferential orders determined over the alternatives) that are obtained using these two different methods. While AHP–TOPSIS is a confident multi-attribute decision-making (MADM) approach which has been successfully applied to many other fields, the similarity validated between these individual results using the proposed method is used to confirm the efficacy of the AHP–GTMA approach and to determine its applicability in practice. In addition, along with this study, some contributable points are also rendered for implementing the decision models, e.g., the optimized recursive implementation in R to compute the permanent value of a square ASAM (alternative selection attribute matrix, which is the computational basis required by AHP–GTMA) of any dimension. The proposed methodological flow to confirm the similarity based on the ordinal rank information is not only convenient in operational practice with ubiquitous tool supports (e.g., the vector-based R statistical platform), but also generalizable (to verify between another pair of results obtained using any other MADM methods). This gives options for future research.

Exploring Location Determinants of Asia’s Unique Beverage Shops Based on a Hybrid Madm Model

“…A number of studies have explored location determinants of various food outlets via various MADM models or statistical analyses. As a classic methodology used in decision-making (dos Santos et al, 2019), AHP is the most commonly used approach in location selection (Tzeng et al, 2002;Özcan et al, 2011;Subramanian & Ramanathan, 2012;Ho et al, 2013;Dehe & Bamfor, 2015;Zhuang et al, 2017Zhuang et al, , 2018aZhuang et al, , 2018bZhuang et al, , 2019Yıldız & Tüysüz, 2019); however, unrealistic assumptions concerning the independent relationship among the concerning factors presents a drawback (Liu et al, 2018a;Shen & Tzeng, 2018;Chen et al, 2019;Lin et al, 2021). Although the Analytic Network Process (ANP) method tries to explore the mutual influence relationship (Tolga et al, 2013;Neumüller et al, 2015), it also lacks a strong support for interactions relationship (Gölcük & Baykasoĝlua, 2016).…”

Competition Among the World’s Main Technological Powers to Develop IPs: Cross-National Longitudinal Patentography Over a 9-Year Time Span

“…These numbers then form a 'statistical variable' that is titled with the abbreviated country name (e.g., GER). Each statistical variable, on the other side, can be treated as a vector whose elements are ordered [26,27] according to a certain sequence of all possible 3-tuples that are prefixed by G or H. Thus, 9 such variables (vectors) are obtained, so the correlation between each pair of these variables is then identified. A so-called 'correlation matrix', which is shown in Table 2, is then obtained by filling in these numbers.…”

Analytic Hierarchy Process as a Tool to Explore the Success Factors of BIM Deployment in Construction Firms

“…Finally, given the confident results in this study, more in-depth future researches can also be expected. For example, the number of interviewees may be increased so the knowledge can be explored on a group basis, while the group/subgroup analytical methods can be introduced [2] [3], as the group-based mind-mining process [11] is a focused cognitive topic in the recent development of datadriven decision-making (DDM, or D 3 M) [12]. In this sense, this study is a pilot work for subsequent studies which pertain to the decision of BIM system adoption.…”