Advanced Mathematical Models & Applications

This paper presents an innovative statistical approach to solve fuzzy multi-objective linear fractional programming problems (FMOLFPP), where decision factors are represented as trapezoidal fuzzy numbers (TrFNs) without transforming to equivalent crisp problems. Each objective function of the FMOLFPP is solved individually, and optimal values are scalarized by statistical measures: mean, median, and standard deviation. Then, by the proposed statistical technique, the FMOLFPP is transformed into a fuzzy linear fractional programming problem (FLFPP), which is subsequently converted into an equivalent fuzzy linear programming problem (FLPP) using Charnes–Cooper variable transformation. The final solution is obtained using the conventional simplex method. To validate the effectiveness of the proposed approach, a numerical example based on a real-world production planning scenario is provided and a comparison with existing methods shows that our statistical framework is reliable.