Multi-attribute group decision making based on cubic bipolar fuzzy information using averaging aggregation operators

Article type: Research Article

Authors: Riaz, Muhammad^{; *} | Tehrim, Syeda Tayyba

Affiliations: Department of Mathematics, University of the Punjab, Lahore, Pakistan

Correspondence: [*] Corresponding author. M. Riaz, Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan. E-mail: [email protected].

Abstract: A huge range of human decisions is involved bipolar subjective thoughts. For illustration, effects and side effects are two different aspects of decision analysis. The equilibrium and mutual coexistence of these two aspects are treated as a key for balanced social environment. A verity of bipolar fuzzy decision making with different technique is available for bipolar fuzzy characterizations of the universe of options that depend on a limited number of grades. So, the concept of simple bipolar fuzzy set is insufficient to provide the information about the occurrence of ranking with accuracy because information is limited. In this regard, we use cubic bipolar fuzzy sets (CBFSs) as the generalization of bipolar fuzzy sets. In human decisions, the second important part is ranking of alternatives obtained after evaluation. The motivation behind this research is to develop an appropriate aggregation method which is simple reliable and efficient enough to handle cubic bipolar fuzzy data. We propose aggregation operators, including, cubic bipolar fuzzy weighted averaging operator, cubic bipolar fuzzy ordered weighted averaging operator and cubic bipolar fuzzy hybrid weighted averaging operator for P -order and R -order. We demonstrate simplicity and efficiency of proposed aggregation operators and algorithm by discussing multi-attribute group decision making (MAGDM) problem under cubic bipolar fuzzy information. We also discuss the comparison analysis of the proposed method of aggregation.

Keywords: Cubic bipolar fuzzy set, Averaging aggregation operators for CBFSs, MAGDM

DOI: 10.3233/JIFS-182751

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2473-2494, 2019