<![CDATA[Data-driven decision-making in the logistics sector often encounters challenges due to fluctuating shipment volumes and unpredictable profit variations. This study implements the Fuzzy Tsukamoto method to process shipment quantity and profit data, enabling a decision-making model that is more responsive to uncertainty. The fuzzification process converts numerical data into fuzzy representations, followed by the application of if-then rules in the inference stage to determine appropriate decisions. The final results are then transformed back into numerical values through the defuzzification process. Evaluation results indicate high accuracy, with a Root Mean Squared Error (RMSE) of 0.07 and a Mean Absolute Error (MAE) of 0.05. These findings suggest that the Fuzzy Tsukamoto method effectively enhances decision-making by accounting for data variations and operational uncertainties. In practical applications, this model can assist logistics companies in optimizing shipment distribution, resource allocation, and delivery planning with greater precision, thereby improving operational efficiency and profitability.]]>
Copyrights © 2025
