Determining the value of the port transport waters: Based on improved TOPSIS model by multiple regression weighting

Wang, Xiaohui; Peng, Bo

doi:10.1016/j.ocecoaman.2015.02.004

Cited by 12 publications

(6 citation statements)

References 5 publications

(5 reference statements)

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“…Each approach suffered from different issue like: in mathematical approach (i.e. normalization [15,16], criteria weight [17], and distance measurement [18,19]). On the other hand, the human approach suffered from the main challenge (i.e.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Salih

Al-qaysi

Shuwandy

et al. 2022

IFS

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To date, for the purpose of solving the complex problems in the area of expert system, Multi criteria decision making is the best technique to offer the suitable solution. In the academic literature, the MCDM methods suffered from many challenges. The most important challenges are uncertainty and vagueness. One of the latest MCDM method, called the fuzzy decision by opinion score method (FDOSM). However, there are still some vagueness issues around these methods (mention some of them). According to the advantage of the Fermatean fuzzy set in solving these issues, in this research extends FDOSM into Fermatean-FDOSM so as to effectively benchmark the real-life problem. In this study, we present our methodology in two phases. The first phase presents the mathematical model of Fermatean-FDOSM which is composed of three stages of FDOSM. The second phase applied the new extension to benchmark the COVID-19 machine learning methods. The finding of Fermatean-FDOSM after comparing the result with the basic FDSOM and TOPSIS, is more logical and undergoing a systematic ranking. In the validation process, objective validation is applied to validate the final result of Fermatean-FDOSM. The result of Fermatean-FDOSM is valid, and more logical and in line with decision makers’ opinions.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Salih

Al-qaysi

Shuwandy

et al. 2022

IFS

View full text Add to dashboard Cite

show abstract

“…Z. He, & Xie, 2016;Kang, Jang, & Park, 2016;Kannan, de Sousa Jabbour, & Jabbour, 2014;Kuo, Wu, & Hsu, 2012;Liu, Chan, & Ran, 2013;Mahdevari, Shahriar, & Esfahanipour, 2014;Mir et al, 2016;Walczak & Rutkowswa, 2017;Wang & Peng, 2015;Zhou, Liu, & Chang, 2016). Other recent studies also extend TOPSIS with the concept of IFS's (Boran et al, 2009;Chen, 2015;J.…”

Section: Ahp and Topsis: The Trustworthy Alternative Approachesmentioning

confidence: 96%

The Effectiveness of if-Madm (Intuitionistic-Fuzzy Multi-Attribute Decision-Making) for Group Decisions: Methods and an Empirical Assessment for the Selection of a Senior Centre

Zhuang

Chang

2019

Technological and Economic Development of Economy

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This study determines the effectiveness of intuitionistic-fuzzy multi-attribute decision-making (IF-MADM) for making group decisions in practice. The effectiveness of the method is measured in terms of four dimensions: applicability, efficacy, efficiency and informativeness. To measure the efficacy, an IF-MADM model that has been recently proposed, AHP and the TOPSIS approach, which are compensatory models for group MADM, are used to model and solve the same collective decision. Using non-parametric statistical tests for data analytics, a ‘similarity confirmation method’ is proposed for a pair-wise test. This is to determine whether the score vectors are similar. Score vectors are used to determine the final ordinal ranks and whose scales differ greatly for different MADM methods. Since the latter two MADM models are both trustworthy with a known range of applications, any similarity in the results verifies the efficacy of IF-MADM. Using this process, the applicability of IF-MADM modelling is demonstrated. The efficiency and informativeness are also benchmarked and justified in terms of the model’s ability to produce a more informed decision. These results are of interest to practitioners for the selection and application of MADM models. Finally, the selection of a senior centre, which is a real group decision problem, is used to illustrate these. This extends the empirical application of IF-MADM, as relatively few studies practically compare issues for IF-MADM with those for other MADM models. The study also supports a rarely studied non-clinical healthcare decision that is relevant because there are many aging societies.

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“…The greater this ratio is, the closer the alternative is to the ideal solution and the farther it is from the anti-ideal solution. Even after 2010, studies using the non-hybrid versions of TOPSIS have been undertaken [24][25][26][27][28][29][30][31][32][33][34][35]. Similar to AHP, these TOPSIS studies can be roughly categorized as methodological improvement studies and model application studies.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2018

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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.

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Determining the value of the port transport waters: Based on improved TOPSIS model by multiple regression weighting

Cited by 12 publications

References 5 publications

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

The Effectiveness of if-Madm (Intuitionistic-Fuzzy Multi-Attribute Decision-Making) for Group Decisions: Methods and an Empirical Assessment for the Selection of a Senior Centre

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

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