Multi-Criteria Decision-Making under mHF ELECTRE-I and HmF ELECTRE-I

Adeel, Arooj; Akram, Muhammad; Koam, Ali N. A.

doi:10.3390/en12091661

Cited by 25 publications

(5 citation statements)

References 53 publications

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“…For the deterministic modeling of decision making problems, many mathematical techniques such as TOPSIS, AHP, VIKOR, ELECTRE and PROMETHEE were developed. These mathematical techniques were adapted for decision making based the fuzzy set and its some extensions [20][21][22][23][24][25][26]. In addition to the fuzzy modelling of TOPSIS, ELECETRE and etc., many algorithmic solutions were proposed for decision making under fuzzy environment [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Three-valued soft set and its multi-criteria group decision making via TOPSIS and ELECTRE

Akçetin

Kamacı²

2020

Scientia Iranica

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The purpose of this paper is to introduce a generalization of Molodtsov's approach to soft sets obtained by passing from the classical two-valued logic underlying those sets to a three-valued logic, where the third truth value can usually be interpreted as either non-determined (i.e., between true and false) or unknown. This extension of soft set approach allows for more intuitive and clearer representation of various decision making problems involving incomplete or uncertain information. In other words, it is a useful way to analyze soft set based multi-criteria group decision making problems under the lack of information resulting from the inability to determine the data. In this paper, we introduce the concept of three-valued soft set and its some operations and products. We propose the formulas to calculate all possible choice values for each object in the (weighted) three-valued soft sets, and thus calculate their respective decision values. By modifying the TOPSIS and ELECTRE methods to deal with multi-criteria group decision problems, three-valued soft set based decision making algorithms are constructed. To demonstrate the practicality of these algorithms, we address the examples adapted from the decision problems in real-life. Lastly, some aspects of the efficiency of the proposed algorithms are discussed with computational experiments.

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Section: Introductionmentioning

confidence: 99%

Three-valued soft set and its multi-criteria group decision making via TOPSIS and ELECTRE

Akçetin

Kamacı²

2020

Scientia Iranica

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“…erefore, it comes as no surprise that multipolarity in data and information plays a vital role in various fields of science and technology. e remarkable contribution on applications of m-polar fuzzy sets is presented in [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

LU Decomposition Scheme for Solving m-Polar Fuzzy System of Linear Equations

Koam

Akram

Muhammad

et al. 2020

Mathematical Problems in Engineering

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This paper presents a new scheme for solving m-polar fuzzy system of linear equations (m-PFSLEs) by using LU decomposition method. We assume the coefficient matrix of the system is symmetric positive definite, and we discuss this point in detail with some numerical examples. Furthermore, we investigate the inconsistent m×nm-polar fuzzy matrix equation (m-PFME) and find the least square solution (LSS) of this system by using generalized inverse matrix theory. Moreover, we discuss the strong solution of m-polar fuzzy LSS of the inconsistent m-PFME. In the end, we present a numerical example to illustrate our approach.

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“…Several researchers explored and discussed MCDM methods based on COMET including [41][42][43]. For other concepts, terminologies, applications and MCDM methods, the readers are referred to [44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method

2019

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The fuzzy linguistic approach provides favorable outputs in several areas, whose description is relatively qualitative. The encouragement for the utilization of sentences or words instead of numbers is that linguistic characterizations or classifications are usually less absolute than algebraic or arithmetical ones. In this research article, we animate the m-polar fuzzy (mF) linguistic approach and elaborate it with real life examples and tabular representation to develop the affluence of linguistic variables based on mF approach. As an extension of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, we develop an m-polar fuzzy linguistic TOPSIS approach for multi-criteria group decision-making (MCGDM). It is used to evaluate the best alternative, to get more authentic and comparable results and to handle the real life problems of having multi-polar information in terms of linguistic variables and values. In this approach decision-makers contribute their estimations in the form of linguistic term sets. To show the efficiency and compatibility of the proposed approach, we compare it with the m-polar fuzzy linguistic ELECTRE-I (Elimination and Choice Translating Reality) approach. Finally, we draw a flow chart of our proposed approach as an algorithm and generate a computer programming code.

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Multi-Criteria Decision-Making under mHF ELECTRE-I and HmF ELECTRE-I

Cited by 25 publications

References 53 publications

Three-valued soft set and its multi-criteria group decision making via TOPSIS and ELECTRE

Three-valued soft set and its multi-criteria group decision making via TOPSIS and ELECTRE

LU Decomposition Scheme for Solving m-Polar Fuzzy System of Linear Equations

Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method

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