scholarly journals A Novel Group Decision Making Method Under Uncertain Multiplicative Linguistic Environment for Information System Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 19848-19855 ◽  
Author(s):  
Jian Lin ◽  
Riqing Chen
Keyword(s):  
Decision Making ◽  
Information System ◽  
Group Decision Making ◽  
Group Decision ◽  
System Selection

scholarly journals FUCOM-MARCOS based Group Decision-making using Dombi Power Aggregation of Dual Probabilistic Linguistic Information

2021 ◽  
Author(s):  
Abhijit Saha ◽  
Arunodaya Raj Mishra ◽  
Pratibha Rani
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Average Operator ◽  
Sensitivity Assessment ◽  
Probabilistic Linguistic Term Sets ◽  
The Stability ◽  
System Selection

Abstract The dual probabilistic linguistic (DPL) term sets are considered superior to probabilistic linguistic term sets. The power average operator can lessen the effects of the extreme assessing data from some decision-makers with prejudice. Further, the Dombi operators are quite flexible with the general parameter during the aggregation process. Moreover, based on deviation from the maximum consistency by the exclusion of the concern of the redundancy of the comparisons made in criteria pairs, FUCOM (full consistency method) is utilized as a subjective criteria weight computing model. Besides, MARCOS (Measurements alternatives and ranking according to compromise solution) method is based on the determination of utility degrees according to the distance from anti-ideal and ideal solutions and their aggregations. In this study, we combine the merits of power average operator, Dombi operator, FUCOM technique and MARCOS for dealing with multi-criteria group decision-making (MCGDM) problems under a DPL setting where rank of the alternatives are obtained through MARCOS method. For aggregating decision-experts preferences, we propose two types of operators, namely- DPL Dombi power weighted averaging and DPL Dombi power weighted geometric aggregation operators. We discuss the elegant properties of these proposed aggregation operators. We provide a case study regarding open source software learning management system selection to focus the practicability and usefulness of the proposed approach. Furthermore, we perform a sensitivity assessment on diverse criteria weight sets in order to test the stability of our developed intriguing approach. To this effect, we also provide a comparison between our approach with various extant methods.

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