Development of Aczel-Alsina Aggregation Operators in Neutrosophic Cubic Sets for Multi-Expert and Multi-Criteria Weighting: Optimizing Alternative Fuel Technology Selection

Managing vague and uncertain data has long been a challenge in decision-making (DM), particularly in scenarios where criteria and expert assessments play a critical role. This paper introduces operational laws based on Aczel-Alsina (AA) norms within Neutrosophic Cubic Sets (NCS) to more effectively handle uncertainty. Leveraging these operational laws, we propose two aggregation operators: the Neutrosophic Cubic Aczel-Alsina Weighted Averaging (NCAAWA) and the Neutrosophic Cubic Aczel-Alsina Weighted Geometric (NCAAWG) operators. These provide a comprehensive approach to data aggregation, preserving both additive and multiplicative influences on outcomes in complex systems. In DM, the importance of weights is paramount, and we introduce a novel method for determining weights based on a model value that represents the entire dataset. This model value serves as a pivotal measure for deriving weights, reflecting the relevance and influence of each input from expert and criteria matrices. The use of model value enhances the intuitiveness and accuracy of the aggregated results. To demonstrate the utility of this approach, we propose a DM method that integrates NCAAWA and NCAAWG with the Weighted Aggregated Sum and Product Assessment Selection (WASPAS) model, allowing for the simultaneous consideration of both additive and multiplicative criteria. This dual approach provides a more comprehensive evaluation of alternatives by accounting for the cumulative and interactive contributions of criteria. The proposed DM method is applied to the evaluation of alternative fuel technologies (AFT), a complex area involving multi-expert criteria that span economic, social, environmental, and technical aspects. The framework offers a balanced and thorough assessment, accommodating the interdependencies and uncertainties inherent in the criteria involved.