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Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas

Publicado 2020-11-15
Artículos

Resumen

Este artículo aborda el problema de selección de portafolio de proyectos para la adjudicación de interventorías de obra pública a través de concursos de méritos abiertos (CMA) supervisados por el Instituto Nacional de Vías (INVIAS) en Colombia. En esta modalidad, cada concursante presenta un portafolio único de proyectos históricos para cuantificar su experiencia como interventor. Como alternativa al uso de hojas de cálculo en Excel con procedimientos limitados de enumeración exhaustiva, se evaluó un algoritmo genético meta-optimizado (GA) y un procedimiento de búsqueda voraz adaptativo probabilista meta-optimizado (GRASP) para el caso de estudio de una Compañía con 207 contratos de trayectoria en el sector. Ambas metaheurísticas consiguieron encontrar puntajes de valoración óptimos para distintas instancias de prueba, sin embargo, el algoritmo GA presentó un mejor desempeño consistentemente en todas las instancias de evaluación, encontrando en algunos casos hasta 10 portafolios óptimos en menos de 9 minutos.

Autores/as

  • Cristian David Candia Garcia

    Escuela Colombiana de Ingeniería Julio Garavito

  • Luis Francisco López Castro

    Escuela Colombiana de Ingeniería Julio Garavito

  • Sonia Alexandra Jaimes Suárez

    Escuela Colombiana de Ingeniería Julio Garavito

Referencias

Agarwal, A., 2018. Multi-echelon Supply Chain Inventory Planning using Simulation-Optimization with Data Resampling. arXiv:1901.00090 [math].

Baykasoğlu, A., Karaslan, F.S., 2017. Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach. International Journal of Production Research 55, 3308–3325. https://doi.org/10.1080/00207543.2017.1306134

Boryssenko, A., Herscovici, N., 2018. Machine Learning for Multiobjective Evolutionary Optimization in Python for EM Problems, in: 2018 IEEE International Symposium on Antennas and Propagation USNC/URSI National Radio Science Meeting. Presented at the 2018 IEEE International Symposium on Antennas and Propagation USNC/URSI National Radio Science Meeting, pp. 541–542. https://doi.org/10.1109/APUSNCURSINRSM.2018.8609394

Cetin, O., 2018. Parallelizing simulated annealing algorithm fot TSP on massively parallel architectures. Journal of Aeronautics and Space Technologies 11, 75–85.

Chen, W., 2015. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem. Physica A: Statistical Mechanics and its Applications 429, 125–139. https://doi.org/10.1016/j.physa.2015.02.060

Colombia Compra Eficiente, 2017. Guía para procesos de contratación de obra pública.

Crawford, B., Soto, R., Cuesta, R., Paredes, F., 2014. Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem [WWW Document]. The Scientific World Journal. https://doi.org/10.1155/2014/189164

Deng, J., Wang, L., 2017. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm and Evolutionary Computation 32, 121–131. https://doi.org/10.1016/j.swevo.2016.06.002

Eshlaghy, A.T., Razi, F.F., 2015. A hybrid grey-based k-means and genetic algorithm for project selection. International Journal of Business Information Systems 18, 141–159. https://doi.org/10.1504/IJBIS.2015.067262

Faezy Razi, F., Shadloo, N., 2017. A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection. Journal of Optimization in Industrial Engineering 10, 49–59. https://doi.org/10.22094/joie.2017.276

Faia, R., Pinto, T., Vale, Z., 2016. GA optimization technique for portfolio optimization of electricity market participation, in: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Presented at the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Athens, Greece, pp. 1–7. https://doi.org/10.1109/SSCI.2016.7849858

Garcia, C., 2014. A metaheuristic algorithm for project selection and scheduling with due windows and limited inventory capacity. Kybernetes 43, 1483–1499. https://doi.org/10.1108/K-11-2013-0245

Ghayour, F., Solimanpur, M., Mansourfar, G., 2015. Optimum portfolio selection using a hybrid genetic algorithm and analytic hierarchy process. Studies in Economics & Finance 32, 379–394. https://doi.org/10.1108/SEF-08-2012-0085

Griffith, A., Pomerance, A., Gauthier, D.J., 2019. Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers. arXiv:1910.00659 [nlin, stat].

Hiassat, A., Diabat, A., Rahwan, I., 2017. A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems 42, 93–103. https://doi.org/10.1016/j.jmsy.2016.10.004

Instituto Nacional de Vías, 2017. Concurso de méritos abierto CMA-DO-SRN-003-2017.

Interian, R., Ribeiro, C.C., n.d. A GRASP heuristic using path-relinking and restarts for the Steiner traveling salesman problem. International Transactions in Operational Research 24, 1307–1323. https://doi.org/10.1111/itor.12419

INVIAS, 2018. Concurso de méritos abierto CMA-DO-SRT-063-2018.

Kumar, M., Mittal, M.L., Soni, G., Joshi, D., 2019. A Tabu Search Algorithm for Simultaneous Selection and Scheduling of Projects, in: Yadav, N., Yadav, A., Bansal, J.C., Deep, K., Kim, J.H. (Eds.), Harmony Search and Nature Inspired Optimization Algorithms, Advances in Intelligent Systems and Computing. Springer Singapore, pp. 1111–1121.

Martínez-Vega, D.A., Cruz-Reyes, L., Rangel-Valdez, N., Santillán, C.G., Sánchez-Solís, P., Villafuerte, M.P., 2019. Project Portfolio Selection with Scheduling: An Evolutionary Approach. 1 10, 25–31.

Mira, C., Feijao, P., Souza, M.A., Moura, A., Meidanis, J., Lima, G., Schmitz, R., Bossolan, R.P., Freitas, I.T., 2012. A GRASP-based Heuristic for the Project Portfolio Selection Problem, in: 2012 IEEE 15th International Conference on Computational Science and Engineering. Presented at the 2012 IEEE 15th International Conference on Computational Science and Engineering (CSE), IEEE, Paphos, Cyprus, pp. 36–41. https://doi.org/10.1109/ICCSE.2012.102

Neumüller, C., Wagner, S., Kronberger, G., Affenzeller, M., 2012. Parameter Meta-optimization of Metaheuristic Optimization Algorithms, in: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (Eds.), Computer Aided Systems Theory – EUROCAST 2011, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 367–374.

Osaba, E., Carballedo, R., Diaz, F., Onieva, E., Lopez, P., Perallos, A., 2014. On the influence of using initialization functions on genetic algorithms solving combinatorial optimization problems: A first study on the TSP, in: 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). Presented at the 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), IEEE, Linz, Austria, pp. 1–6. https://doi.org/10.1109/EAIS.2014.6867465

Panadero, J., Doering, J., Kizys, R., Juan, A.A., Fito, A., 2018. A variable neighborhood search simheuristic for project portfolio selection under uncertainty. Journal of Heuristics. https://doi.org/10.1007/s10732-018-9367-z

Pedersen, M.E.H., 2010. Tuning & Simplifying Heuristical Optimization (phd). University of Southampton.

Resende, M.G.C., Ribeiro, C.C., 2016. Optimization by GRASP. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4939-6530-4

Shadkam, E., Delavari, R., Memariani, F., Poursaleh, M., 2015. Portfolio Selection by the Means of Cuckoo Optimization Algorithm. International Journal on Computational Science & Applications 5, 37–46. https://doi.org/10.5121/ijcsa.2015.5304

Yu, L., Wang, S., Wen, F., Lai, K.K., 2012. Genetic algorithm-based multi-criteria project portfolio selection. Annals of Operations Research 197, 71–86. https://doi.org/10.1007/s10479-010-0819-6

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