Group decision‐making based on pythagorean fuzzy TOPSIS method

2019 ◽  
Vol 34 (7) ◽  
pp. 1455-1475 ◽  
Author(s):  
Muhammad Akram ◽  
Wieslaw A. Dudek ◽  
Farwa Ilyas
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Topsis ◽  
Topsis Method

scholarly journals An Assessment of Objectivity Convergence of Fuzzy TOPSIS Method Extended With Rank Order Weights in Group Decision Making

2018 ◽  
Vol 7 (3) ◽  
pp. 26-33
Author(s):  
Ayan Chattopadhyay ◽  
Upasana Bose
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Rank Order ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Topsis Method ◽  
Chain Management ◽  
Criteria Weights ◽  
Topsis Approach

Group decision making in a multi criteria environment is a familiar business situation where the decision makers identify an ideal choice, among many. The situation gets complex when decision makers do not have crisp data to deal with. The fuzzy TOPSIS method, and its likes, provides solution to such problems and the criteria weight plays a determinant role in the overall priority estimation. This paper presents an extended fuzzy TOPSIS approach by incorporating criteria weights derived from rank order. It considers three criteria weights; the rank order centroid, rank sum and rank reciprocal weights. The criteria weights are calculated separately and integrated with fuzzy TOPSIS method to rank choices. Finally, objectivity convergence of the alternative rankings is tested. The proposed method yields a fairly uniform and consistent result in the case of supply chain management and anticipates wide application in multi criteria environment, concomitant with uncertainty and vagueness.

Doubly Extended Fuzzy TOPSIS Method for Group Decision Making

2019 ◽  
Vol 66 (1) ◽  
pp. 27-50
Author(s):  
Dariusz Kacprzak
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Group Decision Making ◽  
Group Decision ◽  
Real Life ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Final Decision ◽  
Topsis Method ◽  
Decision Matrix

Multiple Criteria Decision Making methods, such as TOPSIS, have become very popular in recent years and are frequently applied to solve many real-life situations. However, the increasing complexity of the decision problems analysed makes it less feasible to consider all the relevant aspects of the problems by a single decision maker. As a result, many real-life problems are discussed by a group of decision makers. In such a group each decision maker can specialize in a different field and has his/her own unique characteristics, such as knowledge, skills, experience, personality, etc. This implies that each decision maker should have a different degree of influence on the final decision, i.e., the weights of decision makers should be different. The aim of this paper is to extend the fuzzy TOPSIS method to group decision making. The proposed approach uses TOPSIS twice. The first time it is used to determine the weights of decision makers which are then used to calculate the aggregated decision matrix for all the group decision matrices provided by the decision makers. Based on this aggregated matrix, the extended TOPSIS is used again, to rank the alternatives and to select the best one. A numerical example illustrates the proposed approach.

Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS method and application to group decision-making program

2018 ◽  
Vol 23 (14) ◽  
pp. 5353-5366 ◽  
Author(s):  
Fazli Amin ◽  
Aliya Fahmi ◽  
Saleem Abdullah
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Topsis ◽  
Topsis Method

scholarly journals Trapezoidal Linguistic Cubic Fuzzy TOPSIS Method and Application in a Group Decision Making Program

2019 ◽  
Vol 29 (1) ◽  
pp. 1283-1300 ◽  
Author(s):  
Aliya Fahmi ◽  
Saleem Abdullah ◽  
Fazli Amin ◽  
Muhammad Aslam ◽  
Shah Hussain
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Hamming Distance ◽  
Fuzzy Topsis ◽  
Multi Criteria Decision Making ◽  
Topsis Method ◽  
Mcdm Method

Abstract The aim of this paper is to define some new operation laws for the trapezoidal linguistic cubic fuzzy number and Hamming distance. Furthermore, we define and use the trapezoidal linguistic cubic fuzzy TOPSIS method to solve the multi criteria decision making (MCDM) method. The new ranking method for trapezoidal linguistic cubic fuzzy numbers (TrLCFNs) are used to rank the alternatives. Finally, an illustrative example is given to verify and prove the practicality and effectiveness of the proposed method.

scholarly journals Multi-Criteria Group Decision-Making Using an m-Polar Hesitant Fuzzy TOPSIS Approach

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 795 ◽  
Author(s):  
Muhammad Akram ◽  
Arooj Adeel ◽  
José Carlos R. Alcantud
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Natural Extension ◽  
Fuzzy Topsis ◽  
Sources Of Information ◽  
Topsis Method ◽  
Hesitant Fuzzy Sets ◽  
Topsis Approach

The m-polar fuzzy sets (mF sets) have a representative and fundamental role in several fields of science and decision-making. The fusion of mF sets with several other theories of mathematics has become a favorable practice for depicting numerous types of uncertainties under multi-polar information. In this article, we introduce an innovative hybrid model, called m-polar hesitant fuzzy sets (mHF-sets), a hybridization of hesitancy and mF sets, which enables us to tackle multi-polar information with hesitancy. Hesitancy incorporates symmetry into the treatment of the data, whereas the m-polar fuzzy format allows for differentiated or asymmetric sources of information. We highlight and explore basic key properties of mHF-sets and formulate intrinsic operations. Moreover, we develop an m-polar hesitant fuzzy TOPSIS (mHF-TOPSIS) approach for multi-criteria group decision-making (MCGDM), which is a natural extension of the TOPSIS method to this framework. We describe applications of mHF-sets in group decision-making. Further, we show the efficiency of our proposed approach by applying it to the industrial field. Finally, we generate a computer programming code that implements our decision-making procedure for ease of lengthy calculations.

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