Abstract
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.
Highlights
Problems that are related to uncertain conditions usually exist in decision-making, but are demanding because of the challenging situation of modeling and handling that comes with such uncertainties
In typical decision-making problems, such as selecting a place to visit, deciding which candidate is suitable for election or choosing the best car to buy, Chen et al [7] introduced the approach of m-polar fuzzy (mF) sets, which is the generalization of bipolar fuzzy sets (BFSs), in which they state that BFSs or 2-polar fuzzy sets are cryptomorphic mathematical approaches
The physical domain is 1–20, For young age, the physical domain is 20–50, For old age, the physical domain is 50–80, For extremely old age, the physical domain is 80–100. We call this linguistic variable a 4-polar fuzzy linguistic variable (4FLV), because we describe a semantic rule M which describes each linguistic value in set V with a 4-polar fuzzy set (4F set)
Summary
Problems that are related to uncertain conditions usually exist in decision-making, but are demanding because of the challenging situation of modeling and handling that comes with such uncertainties. We elaborate the concept of mFLV with graphical representation for an easy understanding and propose the novel approach of mF linguistic TOPSIS to solve the MCGDM problems We define such a decision-making technique to increase the affluence of linguistic variables based on the mF approach and to choose the best alternative without ranking the relation of the remaining alternatives. 1, 2, · · · , r) is responsible for evaluating mFLV of p different alternatives under q linguistic values and the suitable ratings of alternatives are according to all decision-makers, assessed in terms of m different characteristics under the physical domain Pd. The degree of each alternative We present the flow chart of our proposed method of decision-making in Figure 1 as an algorithm
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