Abstract
One of the most important problems faced by any organization is make decisions about how to invest and manage the resources to get more benefits; however, the organizations resources are not enough to support all portfolios proposals. To these problems that face the executives of the big organizations, is known as Select Portfolio Problem. In this work is developed an ant colony algorithm, which is an especially effective meta-heuristic, this meta-heuristic is hybridized with a multi-objective local search, this strategy allows using knowledge of the ant, to build potential solutions, knowledge is obtained through the pheromone trail left by ants when find good solutions, for that the algorithm does not converge prematurely an evaporation strategy is implemented. The strategy meta-heuristic include an optimization model for portfolio selection called discrepancies model, this model is implemented when the information concerned to the quality of the projects is in form of ranking, besides help to evaluate portfolios through ten criteria to maximize the impact of the portfolio. This approach allowed reaching privileged areas of Pareto’s front, where identified solutions that reflect the preferences of the decision maker. The experimental tests show the advantages of our proposal, providing reasonable evidence of its potential for solving the select portfolio problems with many objectives.
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References
Bertolini, M., Braglia, M., Carmignani, G.: Application of the AHP methodology in making a proposal for a public work contract. Int. J. Project Manage. 24(5), 422–430 (2006)
Brans, J.P., Mareschal, B.: PROMETHEE methods. In: Figueira, J., Greco, S., Erghott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 163–190. Springer Science+Business Media, New York (2005)
Cooper, R., Edgett, S., Kleinschmidt, E.: Portfolio management for new product development: results of an industry practices study. R&D Manag. 31(4), 361–380 (2001)
Cruz, L., Fernandez, E., Gomez, C., Rivera, G., Perez, F.: Many-objective portfolio optimization of interdependent projects with ‘a priori’ incorporation of decision-maker preferences. Appl. Math. 8(4), 1517–1531 (2014)
Dey, P.K.: Integrated project evaluation and selection using multiple-attribute decision-making. Int. J. Prod. Econ. 103(1), 90–103 (2006)
Doerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C.: Pareto ant colony optimization: a metaheuristic approach to multiobjective portfolio selection. Ann. Oper. Res. 131(1–4), 79–99 (2004)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolut. Comput. 1(1), 53–66 (1997)
Duarte, B., Reis, A.: Developing a projects evaluation system based on multiple attribute value theory. Comput. Oper. Res. 33(5), 1488–1504 (2006)
Fernandez, E., Felix, L.F., Mazcorro, G.: Multi-objective optimisation of an outranking model for public resources allocation on competing projects. Int. J. Oper. Res. 5(2), 190–210 (2009)
Fernandez, E., Olmedo, R.: Public project portfolio optimization under a participatory paradigm. Appl. Comput. Intell. Soft Comput. 2013, Article ID 891781, 13 p. doi:10.1155/2013/891781 (2013)
Gabriel, S., Kumar, S., Ordoñez, J., Nasserian, A.: A multiobjective optimization model for project selection with probabilistic consideration. Socio-Econ. Plann. Sci. 40(4), 297–313 (2006)
Gómez Santillán, C., Cruz Reyes, L., Meza Conde, E., Schaeffer, E., Castilla Valdez, G.: A self-adaptive ant colony system for semantic query routing problem in P2P networks. Computación y Sistemas (CyS), 13(4) (2010)
Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1), 1–47 (2001)
Halouani, N., Chabchoub, H., Martel, J.M.: PROMETHEE-MD-2T method for project selection. Eur. J. Oper. Res. 195(3), 841–849 (2009)
Kaplan, P., Ranjithan, S.R.: A new MCDM approach to solve public sector planning problems. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multi Criteria Decision Making, pp. 153–159 (2007)
Khalili-Damghani, K., Sadi-Nezhad, S.: A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection. Appl. Soft Comput. 13(1), 339–352 (2013)
Liesio, J., Mild, P., Salo, A.: Preference programming for robust portfolio modeling and project selection. Eur. J. Oper. Res. 181(3), 1488–1505 (2007)
Mavrotas, G., Diakoulaki, D., Caloghirou, Y.: Project prioritization under policy restrictions. A combination of MCDA with 0-1 programming. Eur. J. Oper. Res. 171(1), 296–308 (2006)
Roy, B.: The outranking approach and the foundations of ELECTRE methods. In: Bana e Costa, C.A. (ed.) Reading in Multiple Criteria Decision Aid, pp. 155–183. Springer, Berlin (1990)
Saaty, T.L.: The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. In: Figueira, J., Greco, S., Erghott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 345–407. Springer Science+Bussiness Media, New York (2005)
Sugrue, P., Mehrotra, A., Orehovec, P.M.: Financial aid management: an optimisation approach. Int. J. Oper. Res. 1, 267–282 (2006)
Yang, Y., Yang, S., Ma, Y.: A literature review on decision making approaches for research and development project portfolio selection. In: CSAMSE Conference (2012)
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Samantha Bastiani, S., Cruz-Reyes, L., Fernandez, E., Gómez, C., Rivera, G. (2015). An Ant Colony Algorithm for Solving the Selection Portfolio Problem, Using a Quality-Assessment Model for Portfolios of Projects Expressed by a Priority Ranking. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_28
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DOI: https://doi.org/10.1007/978-3-319-17747-2_28
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