A compromise model for solving fuzzy multiple objective linear programming problems

Two-phase approach had been proposed to generate an efficient solution for the multiple objective linear programming problems [MOLP]. In this research, we shall show a revised two-phase approach to the case of the fuzzy multiple objectives linear programming problems [FMOLP]. This revised model can improve the optimal decision obtained from min operator. Moreover, a compromise model embedded two-phase approach and average operator will be proposed to yield a fuzzy-efficient solution between non-compensatory and fully compensatory. More precisely, one compromise index to the membership function can be adjusted by decision-maker to reveal the way of change on degree of satisfaction for each objective and fuzzy constraint. One numerical example is employed to illustrate our assertions.