ANALISIS PERBANDINGAN METODE FUZZY SIMPLEKS DAN KUMAR DALAM OPTIMASI PRODUKSI FULLY FUZZY LINEAR PROGRAMMING

Abstract

This study discusses a comparative analysis between the fuzzy simplex method and Kumar’s method in solving production optimization problems based on Fully Fuzzy Linear Programming (FFLP). Production optimization problems in real conditions often involve uncertainty in production costs, resource availability, and market demand fluctuations. Therefore, the FFLP approach is applied because all model parameters, including objective functions, constraints, and decision variables, are represented using trapezoidal fuzzy numbers to better describe uncertainty. This research employed a quantitative comparative approach using simulation data with two decision variables. The optimization model was solved using the fuzzy simplex method and Kumar’s method with the Liou–Wang ranking function. The results showed that both methods produced identical optimal solutions, including the same optimal decision variable values, fuzzy profit values, and number of iterations required to reach optimality. However, Kumar’s method provided a simpler pivot selection process through the use of ranking functions, while the fuzzy simplex method was more effective in maintaining the integrity of fuzzy data throughout the iteration process. Based on the comparative analysis, both methods were proven effective for solving FFLP problems, although they differ in computational complexity and operational procedures.