Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment

Alghamdi, M. A.; Alshehri, N. O.; Akram, Muhammad

doi:10.1007/s40815-018-0499-y

Cited by 120 publications

(38 citation statements)

References 20 publications

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“…This section provides a comparative analysis of presented bipolar fuzzy ELECTRE II method with already existing MADM methods such as TOPSIS and ELECTRE I models under bipolar fuzzy environment, which were presented by Alghamdi et al [29]. We applied these methods to the numerical problem presented in Section 3.1, as "selection of business location" to compare the different MCDM methods.…”

Section: Discussion and Comparative Studymentioning

confidence: 99%

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Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model

2019

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The core aim of this paper is to provide a new multiple-criteria decision making (MCDM) model, namely bipolar fuzzy ELimination and Choice Translating REality (ELECTRE) II method, by combining the bipolar fuzzy set with ELECTRE II technique. It can be used to solve the problems having bipolar uncertainty. The proposed method is established by defining the concept of bipolar fuzzy strong, median and weak concordance as well as discordance sets and indifferent set to define two types of outranking relations, namely strong outranking relation and weak outranking relation. The normalized weights of criteria, which may be partly or completely unknown for decision makers, are calculated by using an optimization technique, which is based on maximizing deviation method. A systematic iterative procedure is applied to strongly outrank as well as weakly outrank graphs to determine the ranking of favorable actions or alternatives or to choose the best possible solution. The implementation of the proposed method is presented by numerical examples such as selection of business location and to chose the best supplier. A comparative analysis of proposed ELECTRE II method is also presented with already existing multiple-attribute decision making methods, including Technique for the Order of Preference by Similarity to an Ideal Solution (TOPSIS) and ELECTRE I under bipolar fuzzy environment by solving the problem of business location.

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Section: Discussion and Comparative Studymentioning

confidence: 99%

“…In the last two decades, bipolar fuzzy sets have been studied and applied increasingly by many researchers in different directions. Alghamdi et al [29] introduced the MCDM methods TOPSIS and ELECTRE I, in bipolar fuzzy environment. Akram and Arshad [30] presented a novel trapezoidal bipolar fuzzy TOPSIS method for group decision-making.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model

2019

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“…However, in many cases, the given information is bipolar in nature. Recently, a bipolar fuzzy TOPSIS method for the suitable selection of objects was discussed in [5]. However, in this method, the weights are chosen arbitrarily, which can be changed according to the choice of decision-makers.…”

Section: A View Of Bipolar Fuzzy Extended Topsismentioning

confidence: 99%

“…To solve various real-world decision-making problems, fuzzy and intuitionistic TOPSIS methods were discussed in [3,4]. Alghamdi [5] proposed TOPSIS methods for multi-criteria decision-making methods in a bipolar fuzzy environment and illustrated them with various examples. To deal with the limitations of classical TOPSIS, the researchers in [6][7][8][9][10][11][12][13][14][15][16] have extended the concept of the TOPSIS method under a fuzzy, intuitionistic fuzzy, interval-valued fuzzy and bipolar fuzzy environment for the multi-criteria selection of objects.…”

Section: Introductionmentioning

confidence: 99%

Decision Making Approach Based on Competition Graphs and Extended TOPSIS Method under Bipolar Fuzzy Environment

Sarwar¹,

Akram

Zafar

2018

MCA

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A wide variety of human decision-making is based on double-sided or bipolar judgmental thinking on a positive side and a negative side. This paper develops a new method called bipolar fuzzy extended TOPSIS based on entropy weights to address the multi-criteria decision-making problems involving bipolar measurements with positive and negative values. The extended bipolar fuzzy TOPSIS method incorporates the capability of bipolar information into the TOPSIS to address the interactions between criteria and measure the aggregate values on a bipolar scale. In practical problems, this method can be used to measure the benefits and side effects of medical treatments. We also discuss some novel applications of bipolar fuzzy competition graphs in food webs and present certain algorithms to compute the strength of competition between species.

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“…Since then, several extended TOPSIS methods have been applied to different MCDM problems including [25][26][27]. Further, Alghamdi et al [28] introduced the multi-criteria decision-making methods in the bipolar fuzzy environment. Chu and Lin [29] used the fuzzy TOPSIS method for the selection of robots.…”

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 Methods in Bipolar Fuzzy Environment

Cited by 120 publications

References 20 publications

Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model

Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model

Decision Making Approach Based on Competition Graphs and Extended TOPSIS Method under Bipolar Fuzzy Environment

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

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