A novel fuzzy neural network approach with triangular fuzzy information for the selection of logistics service providers

In this article, we presents a novel fuzzy neural network approach designed to address multi criteria decision making (MCDM) problems, specifically for selecting logistics service providers. The proposed decision making model integrates triangular fuzzy numbers (TFNs) with a triangular fuzzy Einstein weighted averaging (TFEWA) aggregation operator to enhance the decision making process under uncertainty. Initially, we discussed the concept of triangular fuzzy numbers, which allows for the representation of uncertain and imprecise data typically presented in real-world decision making environments. The operational laws, score function, and Hamming distance measures for TFNs are presented to ensure accurate handling of the fuzzy input data. The TFEWA aggregation operator, which is based on Einstein norms and plays a crucial role in aggregating expert opinions in the evaluation process. In the decision making process, we collect expert opinions regarding logistics service providers, expressed as TFNs, which are then processed through the fuzzy neural network model. After that, we apply the proposed decision making model to select the best logistics service providers. The TFEWA operator computes values at the hidden and output layers, and activation functions are applied to produce final output values. These outputs provide a ranked list of logistics service providers based on their overall performance across multiple criteria. The effectiveness of this novel approach is validated through a comparative analysis with existing MCDM methods. The results demonstrate that the triangular fuzzy neural network approach outperforms traditional methods in terms of flexibility, accuracy, and its ability to handle uncertain, fuzzy data. Our method provides a robust decision support system, capable of managing complex decision making tasks in logistics and other fields.