Novel picture fuzzy power partitioned Hamy mean operators with Dempster-Shafer theory and their applications in MCDM

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2024年5月16日

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Tanuja Punetha,
Komal,
Dragan Pamucar

摘要

In some multi-criteria decision-making (MCDM) scenarios, decision makers must address challenges like handling uncertain and incomplete information, managing biases in criteria values, and assessing interrelationships among criteria based on their partitioning as per their characteristics. To tackle these challenges, a picture fuzzy set (PFS) can be utilized to quantify vague information, Hamy mean (HM) can be used to consider criteria interrelationships while the power average (PA) mitigates any kind of biasness. Also, to overcome the limitations of persistence and invariantess in algebraic operations, Dempster-Shafer theory (DST) is employed. By integrating the conventional HM with the traditional PA under partitioning, this paper first introduced the novel power partitioned Hamy mean ( $PPtHM^q$ $P P t H M^{q}$ ) operator. Then, this operator is extended for picture fuzzy numbers (PFNs) with DST and two novel operators are introduced, which are named as picture fuzzy power partitioned Hamy mean $(PFPPtHM^q_{DST})$ $(P F P P t H M_{DST}^{q})$ and picture fuzzy weighted power partitioned Hamy mean $(PFWPPtHM^q_{DST})$ $(P F W P P t H M_{DST}^{q})$ with some desirable properties. Moreover, based on these operators, a new method for MCDM in the PFS environment has been designed. The paper illustrates their application in selecting the best hotel among four alternatives ( $B_1$ $B_{1}$ , $B_2$ $B_{2}$ , $B_3$ $B_{3}$ , $B_4$ $B_{4}$ ) based on five criteria, which are partitioned into two sets. Results indicate that the best and worst alternatives under these operators are hotels $B_1$ $B_{1}$ and $B_4$ $B_{4}$ , respectively. Sensitivity analysis explores the impact of granularity parameter variations, and comparative analysis demonstrates the effectiveness of the presented operators. Overall, the study concludes that these operators offer flexibility, generality, and consistency for analyzing MCDM problems in PFS environments.