A Fuzzy Trade-Off Ranking Method for Multi-Criteria Decision-Making

Jaini, Nor Izzati; Utyuzhnikov, Sergey

doi:10.3390/axioms7010001

Cited by 14 publications

(13 citation statements)

References 64 publications

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“…TOPSIS is based on the idea of an alternative with respect to the closest to the ideal solution and the farthest from the anti-ideal solution as the best option [77]. In this section, TOPSIS, which is considered a proper method amongst MCDM techniques, is utilised.…”

Section: ) Utilising Topsis Methods To Benchmark and Rank English Leamentioning

confidence: 99%

Multi-Criteria Evaluation and Benchmarking for Young Learners’ English Language Mobile Applications in Terms of LSRW Skills

et al. 2019

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This study proposes an evaluation and benchmarking decision matrix (DM) on the basis of multi-criteria decision making (MCDM) for young learners' English mobile applications (E-apps) in terms of listening, speaking, reading and writing (LSRW) skills. Benchmarking E-apps for young learners is challenging due to (a) multiple criteria, (b) criteria importance and (c) data variation. The DM was constructed on the basis of the intersection amongst evaluation criteria in terms of LSRW and E-apps for young learners. The criteria were adopted from a preschool education curriculum standard. The DM data included six E-apps as alternatives and 17 skills as criteria. Thereafter, the six E-apps were evaluated by distributing a checklist form amongst six English learning experts. These apps were subsequently benchmarked by utilising MCDM methods, namely, best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS). BWM was used for criterion weighting, whereas TOPSIS was employed to benchmark and rank the apps. TOPSIS was utilised in two contexts, namely, individual and group. In the group context, internal and external aggregations are applied. Mean was computed to ensure that the E-apps undergo a systematic ranking for objective validation. This study provides scenarios and a benchmarking checklist to evaluate and compare the proposed work with six relative studies. Results indicated that (1) BWM is suitable for criteria weighting. (2) TOPSIS is suitable for benchmarking and ranking E-apps. Moreover, the internal and external TOPSIS group decision making exhibited similar findings, with the best app being 'Montessori' and the worst app being 'FunWithFlupe.' (3) For objective validation, remarkable differences were observed amongst the group scores, which indicate that the internal and external ranking results are identical. (4) In the evaluation, the proposed DM revealed advantages over the six relative studies by 40.00%, 53.33%, 40.00%, 46.67%, 46.67% and 46.67%. INDEX TERMS Language learning app evaluation, language learning app assessment, language teaching/learning strategies. B. B. ZAIDAN received the B.Sc. degree in applied mathematics from Al-Nahrain University, Baghdad, Iraq, in 2004, and the M.Sc. degree in data communications and information security from the University of Malaya, Malaysia, in 2009. He is currently working as a Senior Lecturer with the Department of Computing, University Pendidikan Sultan Idris. He led or has been a member for many funded research projects, and has published more than 150 articles at various international conferences and journals. His research areas are artificial intelligence, decision theory, information security and network, and multicriteria evaluation and benchmarking. O. S. ALBAHRI received the B.Sc. degree in computer science from Al Turath University College, Baghdad, Iraq, in 2011, the M.Sc. degree in computer science and communication from Arts,

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Section: ) Utilising Topsis Methods To Benchmark and Rank English Leamentioning

confidence: 99%

Multi-Criteria Evaluation and Benchmarking for Young Learners’ English Language Mobile Applications in Terms of LSRW Skills

et al. 2019

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“…The main goal of the MCDM method is to obtain the most preferred alternative from a set of feasible alternatives with respective criteria. One of the latest MCDM methods is the trade-off ranking (TOR) method originally introduced by Jaini and Utyuzhnikov [5]. It is an effective method based on the Euclidean distance equation to manage the MCDM problems concerning the DMs' preferences.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is focused on the improved TOR method proposed in [5] by using the Shapley value solution concept in cooperative game theory. The improvement regarding the weights of the criteria is the fairness-based weightage from the Shapley value solution concept.…”

Section: Introductionmentioning

confidence: 99%

A Shapley Trade-Off Ranking Method for Multi-Criteria Decision-Making With Defuzzification Characteristic Function

Ibrahim¹,

Jaini²,

Khalif³

2021

Int. J. of Appl. Math.

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More studies tend to hybrid the game theory technique with the MCDM method to cater to real-situation problems. This paper provides a novel hybrid Shapley value solution concept in the cooperative game with the tradeoff ranking method in MCDM. The fundamental methodology of the Shapley value solution concept and trade-off ranking method are explained to make the methodology clear to the readers. A Shapley trade-off ranking (S-TOR) method has been proposed to obtain the best solution to the fuzzy conflicting MCDM in the personnel selection problem. Thus, the triangular fuzzy number is used to represent the DMs evaluation. Then, the fuzzy number be transformed into crisp values using the defuzzification process. The future suggestions are the fuzzy system may be changed to real data for more practical problems, attempt to incorporate a comprehensive method to increase sharing-profit and decrease sharing-loss in the economy or financial problems, and other types of fuzzy numbers may be used to represent an evaluation of the DMs.

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“…Given a set of objects or individuals, the assumption that each of them may partially fulfill a certain property lies at the grounds of fuzzy set theory. Following the conception of said theory [1], the necessary foundations were developed for solving problems under this new paradigm in order to make it possible to find better solutions to many common modeling problems under the condition of uncertainty [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Assessment of Graphic and 0–10 Rating Scales Used to Measure Entrepreneurial Competences

Vall-llosera

Linares‐Mustarós

Bikfalvi

et al. 2020

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This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using radio buttons). Data are analyzed by means of multitrait–multimethod models estimated as structural equation models with a mean and covariance structure. For the first time in such research, the results include bias, valid variance, method variance, and random error variance. The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers. Neither of the measurement instruments was found to be biased with respect to the other, meaning that their scores are comparable. While both instruments achieve valid and reliable measurements, the reliability and validity are higher for the endecanary scale. This study contributes to the still scarce literature on fuzzy measurement instruments and on the comparability and relative merits of graphic and discrete rating scales on computer-assisted questionnaires.

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A Fuzzy Trade-Off Ranking Method for Multi-Criteria Decision-Making

Cited by 14 publications

References 64 publications

Multi-Criteria Evaluation and Benchmarking for Young Learners’ English Language Mobile Applications in Terms of LSRW Skills

Multi-Criteria Evaluation and Benchmarking for Young Learners’ English Language Mobile Applications in Terms of LSRW Skills

A Shapley Trade-Off Ranking Method for Multi-Criteria Decision-Making With Defuzzification Characteristic Function

A Comparative Assessment of Graphic and 0–10 Rating Scales Used to Measure Entrepreneurial Competences

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