A dual probabilistic linguistic MARCOS approach based on generalized Dombi operator for decision-making

Document Type : Research Paper


1 Department of Engineering Mathematics, College of Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522302, AP, India

2 Department of Mathematics, Government College Raigaon, Satna, M P-485441, India

3 School of Mathematics and Statistics, Southwest University, Beibei-400715, Chongqing, China

4 Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, USA


Dual probabilistic linguistic term sets (DPLTSs) are more powerful compare to probabilistic linguistic term sets, probabilistic hesitant fuzzy sets, hesitant fuzzy sets and intuitionistic fuzzy sets for the reason that they deal with both belongingness grades and non-belongingness grades along with their respective probabilities. On the other hand, the generalized Dombi operators have higher flexibility due to inclusion of two parameters. MARCOS (Measurements alternatives and ranking according to compromise solution) technique was developed by utilizing the utility degrees of options using the ideal and anti-ideal solutions. Here, we combine the merits of generalized Dombi operator and MARCOS and propose a DPL-MARCOS approach under dual probabilistic linguistic setting. In this methodology, the concepts of consistency and similarity between the experts are used to calculate their weights of subjective and objective types, respectively. For aggregating experts' preferences, we propose dual probabilistic linguistic- generalized Dombi weighted averaging aggregation operator. A biomass feedstock selection problem is furnished to show the applicability of our technique. We have considered coconut shell, coffee husk and sugarcane baggage as alternatives. The result shows that coffee husk is the most suitable option. The sensitivity assessment of parameter values reveals that our technique is stable. The comparative study proves that our model is more significant and realistic compare to the existing ones.


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