A decision framework with nonlinear preferences and unknown weight information for cloud vendor selection. (1st March 2023) - Byabartta Kar, Mohuya; Krishankumar, Raghunathan; Pamucar, Dragan; Kar, Samarjit
Highlights: A nonlinear decision approach is put forward for CVS. Attributes are transformed to polynomial space from the linear fuzzy space. Weights of attributes are determined by using CRITIC in the non-linear space. Personalized ranking is obtained with nonlinear WASPAS and rank fusion. Usefulness is validated via a cloud vendor selection case example. Abstract: Cloud vendor selection (CVS) is a complex decision-making problem, which actively adheres to human behavior/cognition. The complex nature of the problem is due to personal biases/hesitation, trade-offs among attributes, uncertainty in rating, and the nonlinear relationship among cloud vendors and associated attributes. In recent times, researchers started paying more attention to user/expert behavior, which led to non-linear decision-making. Most of the extant decision models for CVS considered the linear form of decision-making, which is not realistic due to expert opinions' complexity and dynamism. Motivated by the claim, in this paper, a non-linear decision approach is put forward for CVS. Likert scale rating is adopted for rating cloud vendors based on some attributes, which are transformed to polynomial space from the linear fuzzy space. After this, weights of attributes are determined by using CRITIC in the non-linear space. Following this, cloud vendors are ranked in a personalized fashion using the proposed algorithm that encompasses the WASPAS procedure and rank fusion schemes. Finally, a case study is exemplified to validate the usefulness of the decision approach. Comparison and sensitivity analysis showcases the efficacy and robustness of the developed approach.