Fuzzy analytic network process for evaluating ERP post-implementation alternatives

Ko, Jonghyeon; Comuzzi, Marco

doi:10.1109/fuzz-ieee.2017.8015439

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The two above models were parallelly applied according to the algorithmic stages of each method. It was started with a problem hierarchy structure development, determination of weighting values, vector values calculation, defuzzification, and normalization [49] [50]. The overflow algorithm of F-AHP and F-ANP is explained below.…”

Section: Figure 1 Respondents Distributionmentioning

confidence: 99%

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Decision Support System for Smartphone Recommendation: The Comparison of Fuzzy Ahp and Fuzzy Anp in Multi-Attribute Decision Making

Okfalisa

Rusnedy

Iswavigra

et al. 2020

Sinergi

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Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.

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Section: Figure 1 Respondents Distributionmentioning

confidence: 99%

“…The emphasis of F-ANP is the consideration of the feedback dependency relationship between criteria and within criteria [49] [50]. The working step process as follows [52] [53].…”

Section: Fuzzy Anpmentioning

confidence: 99%