TY  - JOUR
AB  - This paper aims at presenting a new decision framework under an interval-valued probabilistic hesitant fuzzy set (IVPHFS) context with fully unknown weight information. At first, the weights of the attributes are determined by using the interval-valued probabilistic hesitant deviation method. Later, the DMs? weights are determined by using a recently proposed evidence theory-based Bayesian approximation method under the IVPHFS context. The preferences are aggregated by using a newly extended generalized Maclaurin symmetric mean operator under the IVPHFS context. Further, the alternatives are prioritized by using an interval-valued probabilistic hesitant complex proportional assessment method. From the proposed framework, the following significances are inferred; for example, it uses a generalized preference structure that provides ease and flexibility to the decision-makers (DMs) during preference elicitation; weights are calculated systematically to mitigate inaccuracies and subjective randomness; interrelationship among attributes are effectively captured; and alternatives are prioritized from different angles by properly considering the nature of the attributes. Finally, the applicability of the framework is validated by using green supplier selection for a leading bakery company, and from the comparison, it is observed that the framework is useful, practical and systematic for rational decision-making and robust and consistent from sensitivity analysis of weights and Spearman correlation of rank values, respectively.
AU  - Krishankumar, R
AU  - Ravichandran, KS
AU  - Gandomi, AH
AU  - Kar, S
DA  - 2021
DO  - 10.1007/s00521-020-05160-7
EP  - 2457
JO  - Neural Computing and Applications
PB  - Springer
PY  - 2021
SP  - 2445
TI  - Interval-valued probabilistic hesitant fuzzy set-based framework for group decision-making with unknown weight information
VL  - 33
Y1  - 2021
Y2  - 2026/05/11
ER  -