POSSIBILITY-BASED PROBABILITY MAXIMIZATION MODELS FOR FUZZY RANDOM MINIMUM SPANNING TREE PROBLEMS

Abstract

This paper deals with a minimum spanning tree programming problem involving fuzzy random weights. New decision making models are proposed to maximize the probability that the degree of possibility or necessity that the fuzzy goal for the fuzzy random objective function is satisfied is greater than or equal to a satisficing level. It is shown that the original problems involving fuzzy random variables are transformed into deterministic equivalent ones which are nonlinear maximum spanning tree problems through the proposed models. An efficient tabu search algorithm is developed to solve the resulting nonlinear maximum spanning tree problems.