A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods

Sun, Ching-Cherng

doi:10.1016/j.eswa.2010.04.066

Cited by 669 publications

(371 citation statements)

References 51 publications

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“…Based on the above discussion on chosen parameters the following steps are used to rank the components according to their criticality score using Fuzzy TOPSIS method (Sun, 2010).…”

Section: Fuzzy Topsis Methods To Calculate the Subjective Weightmentioning

confidence: 99%

Prioritizing the refactoring need for critical component using combined approach

Sehgal¹,

Mehrotra²,

Bala³

2018

10.5267/j.dsl

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“…Based on the above discussion on chosen parameters the following steps are used to rank the components according to their criticality score using Fuzzy TOPSIS method (Sun, 2010).…”

Section: Fuzzy Topsis Methods To Calculate the Subjective Weightmentioning

confidence: 99%

Prioritizing the refactoring need for critical component using combined approach

Sehgal¹,

Mehrotra²,

Bala³

2018

10.5267/j.dsl

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“…Whenever there is uncertainty in prioritizing one factor over another, fuzzy logic be incorporated with AHP to deal with the complexity. Sun Chia-Chi integrated the fuzzy AHP with Fuzzy TOPSIS [39]. Many other researchers have shown that the fuzzy AHP gives more adequate explanations in the decision-making practice compared to the traditional AHP technique [40].…”

Section: Literature Reviewmentioning

confidence: 99%

A Practical Approach for Evaluating and Prioritizing Situational Factors in Global Software Project Development

Gulzar¹,

Sang²,

Memon³

et al. 2018

ijacsa

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Abstract-There has been an enormous increase in globalization that has led to more cooperation and competition across boundaries. Software engineering, particularly distributed software development (DSD) and global software development (GSD), is evolving rapidly and presents several challenges, such as geographical separations, temporal differences, cultural variations, and management strategies. As a result, a variety of situational factors (SFs) arise that causes challenging problems in software development. Both literature and real world software industry study revealed that the extent of the effect of SFs may vary subject to a certain software project. Project executives should need to concentrate on the right SFs for the successful development of a specific project. This work first examines the optimal and most well-balanced GSD-related SFs and then presents a mechanism for prioritizing the SFs to better understand the extent to which an SF generally affects the GSD. A set of 56 SFs in 11 categories is identified and analyzed in this research. A fuzzy set theory based, multi criteria decision making (MCDM) technique, fuzzy analytical hierarchy process (FAHP) was proposed to extract the SFs that have the strongest effects on GSD. The proposed technique is intelligent and automated and can be customized to suit specific conditions and environments. Thus, it can provide support for a much-needed variation that is the hallmark of such software development environments. A case study of a global company working in collaboration on a project JKL was selected to identify and prioritize the most challenging SFs. A sensitivity analysis is carried out to evaluate the extent of the impact for highly ranked SFs related to JKL project.

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“…The AHP is widely employed for tackling multicriteria decision making problems in real world applications. However, in many practical cases the human preference model is uncertain and evaluator might be reluctant or unable to assign crisp values tothe comparison judgments (Chan & Kumar, 2007).The merit of using a fuzzy approach is to determine the relative importance of attributes using fuzzy numbers instead of precise numbers (On Lit, Soner, 2008;Sun, Lin, 2009;Sun, 2010;Kara, 2011 ).There are many fuzzy AHP methods proposed on the basis of the concepts of the fuzzy set theory and hierarchical structure by various researchers to solve the selection problems in different fields of application (Van Laarhoven and Pedrycz, 1983;Buckley, 1985;Boender et al 1989;Chang, 1996;Cheng, 1996).…”

Section: Fuzzy Ahpmentioning

confidence: 99%

Using A Integrated MCDM Model for Mining Method Selection in Presence of Uncertainty

Yazdani-Chamzini

Yakchali

Zavadskas

2012

Economic Research-Ekonomska Istraživanja

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A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods

Cited by 669 publications

References 51 publications

Prioritizing the refactoring need for critical component using combined approach

Prioritizing the refactoring need for critical component using combined approach

A Practical Approach for Evaluating and Prioritizing Situational Factors in Global Software Project Development

Using A Integrated MCDM Model for Mining Method Selection in Presence of Uncertainty

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