Linear Fractional Programming for Fuzzy Random Based Possibilistic Programming Problem

The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguous. These uncertainties should be included while translating real-world problem into mathematical programming model though handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem hard. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multiobjective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights it's effectiveness.