Hybrid Fuzzy Linear Programming Models for Multi-Objective Complex Decision Problems

Abstract

Complex systems the making of a decision is usually filled with a variety of conflicting goals, uncertainty, and inaccurate knowledge especially in fields like optimization of engineering, resource allocation, and management science. Conventional utilization of optimization models are ineffective in the representation of this ambiguity and complicated models opened are required to harmonize flexibility with the degree of mathematical rigor. The hybrid fuzzy linear programming model is created to solve complex decision problems which have multiple objectives in unpredictable environments. The approach includes the use of fuzzy membership functions to model imprecise parameters and converts several goals into a single framework of satisfaction based. There is a maximization/minimization aggregation technique used to strike a balance among the conflicting objectives still maintaining plausibility within loose constraints. The model is put into a similar crisp model formulation, to be efficient in the computation. It can be seen that the proposed approach is better than the fuzzy linear programming and goal programming as the satisfaction level of 0.85 is higher than 0.78 and the overall performance of the controlled program improves up to 18%. The findings show better strength, flexibility and efficiency of decision making complex multi-objective problems.