A robust decision making algorithm for handling uncertainty in career planning via a circular intuitionistic fuzzy SWARA WASPAS method

Abstract

Abstract Uncertainty, incomplete information, and subjective considerations of various experts usually disrupt decision-making in real-life situations, particularly when optimizing vocational training, career planning, and employment guidance schemes. Conventional Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) models, with their fuzzy counterparts, are based on fixed or point-valued forms and thus are not capable of capturing human hesitations, vagueness, and inconsistency. To mitigate these limitations, this paper presents a hybrid decision-making model that incorporates the use of circular intuitionistic fuzzy set (CIFS) as a part of the SWARA-WASPAS approach. CIFS enables non-membership degrees and membership degrees to be represented as circular regions, acceptably and realistically, of how the experts may articulate uncertainty and hesitation in their evaluations. The CIFS-based SWARA method is used to identify the weights of attributes, and the CIFS-based WASPAS technique is used to evaluate and rank alternatives, using the weighted sum model and weighted product model. The multi-attributes group decision-making (MAGDM) approach proves that the proposed hybrid SWARA- WASPAS method is effective in the context of a hypothetical case study that aims to maximize vocational training, career planning, and employment guidance programs. The findings reveal that “vocational training combined with basic career counseling” always tops the list of all the models. The framework is also stable and robust, as indicated by sensitivity analysis and Spearman rank correlation. The comparative analysis demonstrates that the proposed SWARA–WASPAS method outperforms traditional and fuzzy variants by offering superior uncertainty modeling and greater robustness.