Article ID: | iaor2005859 |
Country: | Netherlands |
Volume: | 131 |
Issue: | 1 |
Start Page Number: | 79 |
End Page Number: | 99 |
Publication Date: | Oct 2004 |
Journal: | Annals of Operations Research |
Authors: | Hartl Richard F., Strauss Christine, Gutjahr Walter J., Stummer Christian, Doerner Karl |
Keywords: | optimization: simulated annealing, programming: multiple criteria |
Selecting the “best” project portfolio out of a given set of investment proposals is a common and often critical management issue. Decision-makers must regularly consider multiple objectives and often have little a priori preference information available to them. Given these constraints, they can improve their chances of achieving success by following a two-phase procedure that first determines the solution space of all efficient (i.e., Pareto-optimal) portfolios and then allows them to interactively explore that space. However, the task of determining the solution space is not trivial: brute-force complete enumeration only works for small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. Meta-heuristics provide a useful compromise between the amount of computation time necessary and the quality of the approximated solution space. This paper introduces Pareto Ant Colony Optimization as an especially effective meta-heuristic for solving the portfolio selection problem and compares its performance to other heuristic approaches (i.e., Pareto Simulated Annealing and the Non-Dominated Sorting Genetic Algorithm) by means of computational experiments with random instances. Furthermore, we provide a numerical example based on real world data.