Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection

One of the most important, common and critical management issues lies in determining the "best" project portfolio out of a given set of investment proposals. As this decision process usually involves the pursuit of multiple objectives amid a lack of a priori preference information, its quality can be improved by implementing a two-phase procedure that first identifies the solution space of all efficient (i. e., Pareto-optimal) portfolios and then allows an interactive exploration of that space. However, determining the solution space is not trivial because brute-force complete enumeration only solves small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. While metaheuristics in general provide an attractive compromise between the computational effort necessary and the quality of an approximated solution space, Pareto Ant Colony Optimization (P-ACO) has been shown to perform particularly well for this class of proble...

• Type: Journal
• Year: 2006
• Where: EOR
• Authors: Karl F. Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss, Christian Stummer
• Keywords: Ant Colony Optimization, EOR 2006, Integer Linear Programming, Solution Space