抄録
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.
