Abstract
The problems of the real world, within which the variable time is present, have involved continuous changes. These problems usually change over time in their objectives, constraints or parameters. Therefore, it is necessary to carry out a readjustment when calculating their solution. This paper proposes an original way of approaching the project portfolio selection problem enriched with dynamic allocation of resources. A new mathematical model is proposed formulating this multi-objective optimization problem, as well as its exact and approximate solution, the latter based on four of the algorithms that in our opinion stand out in the state of the art: Archive-Based hybrid Scatter Search, MultiObjective Cellular, Nondominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2. We experimentally demonstrate the benefits of our proposal and leave open the possibility that its study will apply to large-scale problems.
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References
K. Weicker, Evolutionary algorithms and dynamic optimization problems (Der Andere Verlag, Berlin, 2003)
C.P.A. Santos, I.A. Lopez-Sanchez. Portfolio Generation Based on a Dynamic Allocation of Resources. U.S. Patent Application No. 14/485,339 (2014)
J. Pajares, A. López, A. Araúzo, C. Hernández, Project Portfolio Management, selection and scheduling. Bridging the gap between strategy and operations, in XIII Congreso de Ingeniería de Organización (pp. 1421–1429) (2009, April)
A.J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J.J. Durillo, A. Beham, AbYSS: adapting scatter search to multiobjective optimization. IEEE Trans. Evol. Comput. 12(4), 439–457 (2008)
A.J. Nebro, J.J. Durillo, F. Luna, B. Dorronsoro, E. Alba, Design issues in a multiobjective cellular genetic algorithm, in International Conference on Evolutionary Multi-Criterion Optimization. (Springer Berlin Heidelberg, 2007, March) (pp. 126–140)
K. Deb, A. Pratap, S. Agarwal, T.A.M.T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
E. Zitzler, M. Laumanns, L. Thiele, SPEA2: improving the strength Pareto evolutionary algorithm (2001)
A. Nebro, F. Luna, B. Dorronsoro, J. Durillo, Un algoritmomultiobjetivobasadoen búsqueda dispersa. Quinto Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2007), pp. 175–182 (2007)
K.R. Davis, R. Davis, P.G. Mckeown, Modelos cuantitativos para administración. Grupo Editorial Iberoamérica (1986)
P. Sánchez, Propuesta de anteproyecto de tesis: Nuevos métodos de incorporación de preferencias en metaheurísticas multiobjetivo para la solución de problemas de cartera de proyectos (2012)
H. Jain, K. Deb, An improved adaptive approach for elitist nondominated sorting genetic algorithm for many-objective optimization, in International Conference on Evolutionary Multi-Criterion Optimization (Springer Berlin Heidelberg, 2013, March) (pp. 307–321)
J.J. Durillo, A.J. Nebro, jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw, 42(10), 760–771 (2011)
I. Rodríguez-Fdez, A. Canosa, M. Mucientes, A. Bugarín, STAC: a web platform for the comparison of algorithms using statistical tests. in Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on (2015, August) (pp. 1–8). IEEE
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Martínez-Vega, D.A., Cruz-Reyes, L., Gomez-Santillan, C., Rangel-Valdez, N., Rivera, G., Santiago, A. (2018). Modeling and Project Portfolio Selection Problem Enriched with Dynamic Allocation of Resources. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_26
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DOI: https://doi.org/10.1007/978-3-319-71008-2_26
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