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

Vista sencilla de ítem
dc.contributor.authorCandia Garcia, Cristian Davidspa
dc.contributor.authorLópez Castro, Luis Franciscospa
dc.contributor.authorJaimes Suárez, Sonia Alexandraspa
dc.date.accessioned2020-06-21 00:00:00
dc.date.accessioned2022-06-17T20:20:45Z
dc.date.available2020-06-21 00:00:00
dc.date.available2022-06-17T20:20:45Z
dc.date.issued2020-06-21
dc.description.abstractEste 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.spa
dc.description.abstractThis article addresses the problem of project portfolio selection for the awarding of public works audits through open merit competitions (CMA) supervised by the National Roads Institute in Colombia - INVIAS. In this modality, each competitor presents a unique portfolio of historical projects to quantify its experience. As an alternative to the use of Excel spreadsheets with limited procedures of exhaustive enumeration, a meta-optimized genetic algorithm (GA) and a meta-optimized greedy randomized adaptive search procedure (GRASP) were evaluated for the case study of a company with 207 experience career contracts. Both metaheuristics were able to find optimal assessment scores for different test instances, however, the GA algorithm consistently performed better in all assessment instances, finding in some cases up to 10 optimal portfolios in less than 9 minutes.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.24050/reia.v17i34.1399
dc.identifier.eissn2463-0950
dc.identifier.issn1794-1237
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/5112
dc.identifier.urlhttps://doi.org/10.24050/reia.v17i34.1399
dc.language.isospaspa
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.relation.bitstreamhttps://revistas.eia.edu.co/index.php/reveia/article/download/1399/1349
dc.relation.citationeditionNúm. 34 , Año 2020spa
dc.relation.citationendpage18
dc.relation.citationissue34spa
dc.relation.citationstartpage1
dc.relation.citationvolume17spa
dc.relation.ispartofjournalRevista EIAspa
dc.relation.referencesAgarwal, A., 2018. Multi-echelon Supply Chain Inventory Planning using Simulation-Optimization with Data Resampling. arXiv:1901.00090 [math].spa
dc.relation.referencesBaykasoğlu, 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/10.1080/00207543.2017.1306134spa
dc.relation.referencesBoryssenko, 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.2018.8609394spa
dc.relation.referencesCetin, O., 2018. Parallelizing simulated annealing algorithm fot TSP on massively parallel architectures. Journal of Aeronautics and Space Technologies 11, 75–85.spa
dc.relation.referencesChen, 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.2015.02.060spa
dc.relation.referencesColombia Compra Eficiente, 2017. Guía para procesos de contratación de obra pública.spa
dc.relation.referencesCrawford, 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/10.1155/2014/189164spa
dc.relation.referencesDeng, 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.2016.06.002spa
dc.relation.referencesEshlaghy, 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.2015.067262spa
dc.relation.referencesFaezy 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.2017.276spa
dc.relation.referencesFaia, 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.2016.7849858spa
dc.relation.referencesGarcia, 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-11-2013-0245spa
dc.relation.referencesGhayour, 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-08-2012-0085spa
dc.relation.referencesGriffith, A., Pomerance, A., Gauthier, D.J., 2019. Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers. arXiv:1910.00659 [nlin, stat].spa
dc.relation.referencesHiassat, 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.2016.10.004spa
dc.relation.referencesInstituto Nacional de Vías, 2017. Concurso de méritos abierto CMA-DO-SRN-003-2017.spa
dc.relation.referencesInterian, 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.12419spa
dc.relation.referencesINVIAS, 2018. Concurso de méritos abierto CMA-DO-SRT-063-2018.spa
dc.relation.referencesKumar, 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.spa
dc.relation.referencesMartínez-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.spa
dc.relation.referencesMira, 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.2012.102spa
dc.relation.referencesNeumüller, 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.spa
dc.relation.referencesOsaba, 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.2014.6867465spa
dc.relation.referencesPanadero, 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-zspa
dc.relation.referencesPedersen, M.E.H., 2010. Tuning & Simplifying Heuristical Optimization (phd). University of Southampton.spa
dc.relation.referencesResende, M.G.C., Ribeiro, C.C., 2016. Optimization by GRASP. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4939-6530-4spa
dc.relation.referencesShadkam, 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.2015.5304spa
dc.relation.referencesYu, 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/s10479-010-0819-6spa
dc.rightsRevista EIA - 2020spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1399spa
dc.subjectalgoritmo genéticospa
dc.subjectGRASPspa
dc.subjectmeta-optimizaciónspa
dc.subjectselección de portafolio de proyectosspa
dc.subjectOptimizaciónspa
dc.subjectMetaheurísticasspa
dc.subjectMeta-optimizaciónspa
dc.subjectgenetic algorithmseng
dc.subjectGRASPeng
dc.subjectmeta-optimizationeng
dc.subjectproject portfolio selectioneng
dc.titleSelección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadasspa
dc.title.translatedOptimal Project Portfolio Selection Using Meta-Optimized Population and Trajectory-Based Metaheuristicseng
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTREFspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dspace.entity.typePublication
Archivos
Colecciones
Revista EIA (Portal de Revistas)