Pythagorean fuzzy multi-criteria decision making method based on CODAS with new score function

Peng, Xindong; Ma, Xueling

doi:10.3233/jifs-190043

Cited by 31 publications

(26 citation statements)

References 62 publications

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“…About 113 number of questionnaires were distributed among the employees, out of which 58 numbers (not returned) and 55 numbers (returned) about 48.67 percentage responses acquired. The above-mentioned Table 1 shows the collected data of PPM practices based on the perceptions of various professionals working in the industry [17,18].…”

Section: Resultsmentioning

confidence: 99%

Analysis on Project Portfolio Management Practices in Indian Construction Industry

Vinayagam¹,

Hemprashant²,

Sruthy³

et al. 2021

JCEST

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Project Portfolio management (PPM) is a combination of projects under the sponsorship of a particular construction organization sharing the scarce resources, managing projects and programs within the portfolio. It requires different strategies, models and practices. Many organizations across the country have projects in their sector in different places. However they abandoned temporarily suspended or closed within a decade which is troublesome. Proper PPM helps to execute the construction project effectively. As such, the aim of this research paper is to identify PPM practices in different construction organizations with a view to examine the effects of such practices on the project portfolio. The current research topic focuses on analysing the project performance of different construction projects using Project Portfolio Management practices. In this research a questionnaire survey related to the Project Portfolio Management on four major practices is carried out among the various professionals in Indian Construction Industry with help of Multi Criteria Decision Making (MCDM) techniques such as Entropy Method, SAW, CODAS methods and ranking the various project portfolio.

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Section: Resultsmentioning

confidence: 99%

Analysis on Project Portfolio Management Practices in Indian Construction Industry

Vinayagam¹,

Hemprashant²,

Sruthy³

et al. 2021

JCEST

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“…Further studies will extend the proposed method to the interval‐valued Pythagorean fuzzy set, soft set, q ‐rung orthopair fuzzy sets, and other uncertain and fuzzy environment …”

Section: Discussionmentioning

confidence: 99%

Pythagorean fuzzy interaction power Bonferroni mean aggregation operators in multiple attribute decision making

2019

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The power Bonferroni mean (PBM) operator can relieve the influence of unreasonable aggregation values and also capture the interrelationship among the input arguments, which is an important generalization of power average operator and Bonferroni mean operator, and Pythagorean fuzzy set is an effective mathematical method to handle imprecise and uncertain information. In this paper, we extend PBM operator to integrate Pythagorean fuzzy numbers (PFNs) based on the interaction operational laws of PFNs, and propose Pythagorean fuzzy interaction PBM operator and weighted Pythagorean fuzzy interaction PBM operator. These new Pythagorean fuzzy interaction PBM operators can capture the interactions between the membership and nonmembership function of PFNs and retain the main merits of the PBM operator. Then, we analyze some desirable properties and particular cases of the presented operators. Further, a new multiple attribute decision making method based on the proposed method has been presented. Finally, a numerical example concerning the evaluation of online payment service providers is provided to illustrate the validity and merits of the new method by comparing it with the existing methods. K E Y W O R D S interaction operational laws, multiple attribute decision making, PBM operator, PFIPBM operator, Pythagorean fuzzy set 1 | INTRODUCTIONMultiple attribute decision making (MADM) is an important research topic in decision science, which can be described as to select the best alternative from a set of feasible alternatives according to several attributes. Due to the increasing complexity and uncertainty in modern decision making activities, some influencing factors result in decision makers to often have difficulty in assigning crisp values or fuzzy values 1 described only by membership degree for each alternative with respect to each attribute. More recently, as an effective generalization of intuitionistic fuzzy sets (IFSs), 2 the concept of Pythagorean fuzzy sets (PFSs) was developed by Yager. 3,4 The PFSs expressed fuzzy information by using a membership degree and a nonmembership degree, and satisfied the condition that the square sum of its membership degree and nonmembership degree is less than or equal to 1. Obviously, PFSs are more powerful than fuzzy sets (FSs) and IFSs to describe some complex fuzzy information. [5][6][7][8] In recent years, as a novel evaluation, PFSs not only have been extensively studied in theory, but also have achieved a great number of achievements in applications, such as information measures, [9][10][11][12][13][14][15] basic operations, [16][17][18]11,13,[19][20][21][22][23][24][25][26][27][28] Especially the information aggregation operators, as an important role in MADM, have been proposed to aggregate Pythagorean fuzzy numbers (PFNs). For instance, Ma and Xu 29 developed the Pythagorean fuzzy weighted averaging (PFWA) operator and Pythagorean fuzzy weighted geometric (PFWG) operator, and also proposed symmetric Pythagorean fuzzy weighted averaging (SPFWA) operator and symmetric ...

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“…For instance, [13] developed MADM method based on TOPSIS and similarity measures under IFS environment. Peng and Ma [37] put forward a novel score function, a MADM method based on it and combinative distance-based assessment (CODAS) using pythagorean fuzzy sets and applied their proposed technique for making decision regarding teaching evaluation system. [44] proposed multi-attributive border approximation area comparison (MABAC) based MAGDM method under qrung orthopair fuzzy environment and further applied it in choosing construction projects.…”

Section: Introductionmentioning

confidence: 99%

An efficient intuitionistic fuzzy MULTIMOORA approach based on novel aggregation operators for the assessment of solid waste management techniques

Garg

Rani

2021

Appl Intell

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This paper intends to propose a multi-attribute group decision making (MAGDM) methodology based on MULTIMOORA under IFS theory for application in the assessment of solid waste management techniques. The present work is divided into three folds. The first fold is that some novel operational laws for intuitionistic fuzzy numbers are introduced and a series of aggregation operators based on them are developed. The properties related to new operations are discussed in detail. The second fold is that particle swarm optimization (PSO) algorithm is applied for attribute weight determination by formulating a non-linear optimization model with the goal of maximizing the distance of each alternative from negative ideal solution and minimizing the distance from positive ideal solution. Lastly, a MAGDM method based on MULTIMOORA is put forward and is applied in ranking different solid waste management techniques by taking various social, economical, environmental and technological factors into consideration. The reliability and effectiveness of the proposed methodology is explored by comparing the obtained results with several existing studies. The sensitivity analysis is done by taking different parameter values in order to show the stability of the proposed method.

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Pythagorean fuzzy multi-criteria decision making method based on CODAS with new score function

Cited by 31 publications

References 62 publications

Analysis on Project Portfolio Management Practices in Indian Construction Industry

Analysis on Project Portfolio Management Practices in Indian Construction Industry

Pythagorean fuzzy interaction power Bonferroni mean aggregation operators in multiple attribute decision making

An efficient intuitionistic fuzzy MULTIMOORA approach based on novel aggregation operators for the assessment of solid waste management techniques

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