A Novel Hesitant Intuitionistic Fuzzy DEMATEL-TOPSIS Model for Cloud Service Provider Selection

Aazagreyir, Paul; Appiahene, Peter; Appiah, Obed; Boateng, Samuel T.

doi:10.2139/ssrn.4384253

Cited by 2 publications

(2 citation statements)

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“…Author Year Applied on Techniques compared Similarity measure performed [6] 2019 Supplier selection Fuzzy analytic hierarchy process (FAHP), FT, and FV No [9] 2021 House selection problem FT, FV, FWASPAS, and fuzzy measure (FM) Yes [8] 2022 Dentistry biomaterial selection AHP, followed by the use of AHP-VIKOR, AHP-TOPSIS, AHP-MOORA, AHP-ELECTRE, and AHP-PROMTHEE Yes [11] 2022 Supplier selection TOPSIS, VIKOR, COMET, SPOTIS, and MARCOS Yes [7] 2023 Private tutor selection AHP-SAW, AHP-WP, and AHP-TOPSIS No [10] 2019 Web service selection SAW, AHP, TOPSIS VIKOR, and COPRAS Yes [12] 2023 Cloud service provider selection Hesitant intuitionistic FD-TOPSIS model No [13] 2021 Cloud environment AHP, PROMETHEE, TOPSIS, and VIKOR No [14] 2022 Clean energy evaluation SAW, TOPSIS, ELECTRE, VIKOR and COPRAS No [15] 2020 Web applications security design assessment…”

Section: Fuzzy Decision-making Trial and Evaluation Laboratorymentioning

confidence: 99%

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Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection

Aazagreyir,

Appiahene,

Appiah

et al. 2024

IJ-AI

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This research aims to compare and analyze the effectiveness of four popular fuzzy multi-criteria decision-making methods (FMCDMMs) for quality of service (QoS)-based web service selection. These methods are fuzzy DEMATEL (FD), fuzzy TOPSIS (FT), fuzzy VIKOR (FV), and fuzzy PROMETHEE (FP), including three ranking versions of FV. We assess the ranking similarities among these methods using Spearman's relationship figure. We describe the algorithms of these six FMCDMs in the methods section. In a case study, we collected primary data from five experts who rated nine QoS factors of nine web services. We used modified online software for analysis. The results showed that S6 ranked first in all FMCDMs, except for FD and FP, where it was ranked 2nd and 8th, respectively. The highest association coefficient (Rs) was found between FT and FV ranking in S techniques (0.983), FV ranking in S and FV ranking in Q (0.883), and FT and FV ranking Q (0.833) when comparing the similarity measure of the FMCDMMs. This analysis helps decision-makers and researchers choose the most suitable methods for integrated FMCDMs studies and real-world problem-solving.

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Section: Fuzzy Decision-making Trial and Evaluation Laboratorymentioning

confidence: 99%

“…-Step 4: crisp values obtained via defuzzification. CFCS technique was proposed by Opricovic and Tzeng for getting total-influence matrix figures with the steps in (9)(10)(11)(12)(13)(14)(15).…”

Section: Fuzzy Decision-making Trial and Evaluation Laboratorymentioning

confidence: 99%