Ordered Weighted Average optimization in Multiobjective Spanning Tree Problem

Multiobjective Spanning Tree Problems are studied in this paper. The ordered median objective function is used as an averaging operator to aggregate the vector of objective values of feasible solutions. This leads to the Ordered Weighted Average Spanning Tree Problem, a nonlinear combinatorial optimization problem. Different mixed integer linear programs are proposed, based on the most relevant minimum cost spanning tree models in the literature. These formulations are analyzed and several enhancements presented. Their empirical performance is tested over a set of randomly generated benchmark instances. The results of the computational experiments show that the choice of an appropriate formulation allows to solve larger instances with more objectives than those previously solved in the literature.