New multiobjective metaheuristic solution procedures for capital investment planning

Capital investment planning is a periodic management task that is particularly challenging in the presence of multiple objectives as trade-offs have to be made with respect to the preferences of the decision-makers. The underlying mathematical model is a multiobjective combinatorial optimization problem that is NP-hard. One way to tackle this problem is first to determine the set of all efficient portfolios and then to explore this set in order to identify a final preferred portfolio. In this study, we developed heuristic procedures to find efficient portfolios because it is impossible to enumerate all of them within a reasonable computation time for practical problems. We first added a neighborhood search routine to the Pareto Ant Colony Optimization (P-ACO) procedure to improve its performance and then developed a Tabu Search procedure and a Variable Neighborhood Search procedure. Step-by-step descriptions of these three new procedures are provided. Computational results on benchmark and randomly generated test problems show that the Tabu Search procedure outperforms the others if the problem dos not have too many objective functions and an excessively large efficient set. The improved P-ACO procedure performs better otherwise.