Decision support for planning and resource allocation in hierarchical organizations
IEEE Transactions on Systems, Man and Cybernetics
An aspiration-level interactive model for multiple criteria decision making
Computers and Operations Research - Special issue: implementing multiobjective optimization methods: behavioral and computational issues
Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Computers and Operations Research
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Scatter search and path relinking
New ideas in optimization
Future Generation Computer Systems
Tabu Search
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A flexible decision support system for steel hot rolling mill scheduling
Computers and Industrial Engineering - Special issue: Selected papers from the 25th international conference on computers & industrial engineering in New Orleans, Louisiana
Variable neighborhood search for nurse rostering problems
Metaheuristics
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Interactive selection of Web services under multiple objectives
Information Technology and Management
Large-scale public R&D portfolio selection by maximizing a biobjective impact measure
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Journal of Computer and Systems Sciences International
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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 does not have too many objective functions and an excessively large efficient set. The improved P-ACO procedure performs better otherwise.