A novel cloud model based on multiplicative unbalanced linguistic term set

The cloud decision support model is an effective technique for describing an ambiguous mapping between qualitative notions and their quantitative manifestations. However, the existing research on cloud models is based on balanced linguistic term set and cannot address information in an unbalanced linguistic form. Henceforth, to thoroughly capture the intrinsic fuzziness and vagueness of decision information an accurate decision model is required to acquire good solution. In this direction, the proposed work aims to introduce a cloud model based on multiplicative unbalanced linguistic term sets. To match our research requirements, we initially propose a multiplicative unbalanced point-based cloud model. The innovation of the model overcomes the inherent limitation of the existing cloud and is competent of producing new solution according to the cloud model theory. Furthermore, a generalized multiplicative unbalanced linguistic scale function is introduced. The proposed generalized function creates a map between the linguistic data and numerical interval values. Based on this rational conversion method, interval cloud variables are transformed, which overcomes the loss of information existing in the point-based cloud models where linguistic variables are transformed into a specific numerical value. The process is then used to demonstrate the accuracy and efficacy of the suggested model using a numerical example, which is then followed by a comparison study. The overall results typically show that the cloud model has excellent performance and provides better solutions.