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Article

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

1
Department of Civil Engineering, College of Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan (ROC)
2
Department of Management Sciences, College of Business and Management, Tamkang University, New Taipei City 251, Taiwan (ROC)
3
Department of Multimedia Design, College of Human Ecology and Design, St John’s University, New Taipei City 251, Taiwan (ROC)
4
The Graduate Institute of Business and Management, College of Management, Chang Gung University, Taoyuan City 333, Taiwan (ROC)
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(10), 1974; https://doi.org/10.3390/app8101974
Received: 4 September 2018 / Revised: 7 October 2018 / Accepted: 12 October 2018 / Published: 18 October 2018
(This article belongs to the Special Issue Selected Papers from IEEE ICICE 2018..)
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. View Full-Text
Keywords: comparative research flow for MADM; ordinal rank based; multi-measure; graph theory and matrix approach (GTMA); TOPSIS; recursive function; paper shredder selection comparative research flow for MADM; ordinal rank based; multi-measure; graph theory and matrix approach (GTMA); TOPSIS; recursive function; paper shredder selection
MDPI and ACS Style

Zhuang, Z.-Y.; Lin, C.-C.; Chen, C.-Y.; Su, C.-R. Rank-Based Comparative Research Flow Benchmarking the Effectiveness of AHP–GTMA on Aiding Decisions of Shredder Selection by Reference to AHP–TOPSIS. Appl. Sci. 2018, 8, 1974. https://doi.org/10.3390/app8101974

AMA Style

Zhuang Z-Y, Lin C-C, Chen C-Y, Su C-R. Rank-Based Comparative Research Flow Benchmarking the Effectiveness of AHP–GTMA on Aiding Decisions of Shredder Selection by Reference to AHP–TOPSIS. Applied Sciences. 2018; 8(10):1974. https://doi.org/10.3390/app8101974

Chicago/Turabian Style

Zhuang, Zheng-Yun, Chang-Ching Lin, Chih-Yung Chen, and Chia-Rong Su. 2018. "Rank-Based Comparative Research Flow Benchmarking the Effectiveness of AHP–GTMA on Aiding Decisions of Shredder Selection by Reference to AHP–TOPSIS" Applied Sciences 8, no. 10: 1974. https://doi.org/10.3390/app8101974

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