Transportation problems often involve uncertainty in costs, supply, and demand parameters. This study presents a double-parametric framework for solving fully fuzzy transportation problems (FFTPs) using triangular fuzzy numbers (TFNs). By combining the \(\alpha \) -cut concept together with a secondary parameter \(\beta \) , the fuzzy transportation model is transformed into a family of parametric transportation problems. For each parameter pair \((\alpha ,\beta )\in [0,1]\times [0,1]\) , an initial basic feasible solution is obtained using the TOCM–MT method [1]. The proposed framework enables systematic analysis of transportation cost behaviour under uncertainty through a bounded spectrum of feasible solutions. For the primary benchmark problem, the transportation cost ranges from 234 to 1314, with a center value of 743. The framework was further validated using ten benchmark problems under three uncertainty spread levels \((k=1,3,5)\) . The results show that increasing uncertainty systematically enlarges the feasible transportation cost interval, whereas the center transportation cost remains unchanged for each problem. In addition, sensitivity analysis based on transportation cost bounds demonstrates the influence of uncertainty on transportation cost variability across different spread levels. These findings show that the proposed framework provides a practical approach for analysing optimistic, intermediate, and pessimistic transportation scenarios in fully fuzzy transportation environments.
