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

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 portafo...

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Autores:: Candia Garcia, Cristian David
López Castro, Luis Francisco
Jaimes Suárez, Sonia Alexandra

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Universidad EIA .

Repositorio:: Repositorio EIA .

Idioma:: spa

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dc.title.spa.fl_str_mv	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
dc.title.translated.eng.fl_str_mv	Optimal Project Portfolio Selection Using Meta-Optimized Population and Trajectory-Based Metaheuristics
title	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
spellingShingle	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas algoritmo genético GRASP meta-optimización selección de portafolio de proyectos Optimización Metaheurísticas Meta-optimización genetic algorithms GRASP meta-optimization project portfolio selection
title_short	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
title_full	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
title_fullStr	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
title_full_unstemmed	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
title_sort	Selección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadas
dc.creator.fl_str_mv	Candia Garcia, Cristian David López Castro, Luis Francisco Jaimes Suárez, Sonia Alexandra
dc.contributor.author.spa.fl_str_mv	Candia Garcia, Cristian David López Castro, Luis Francisco Jaimes Suárez, Sonia Alexandra
dc.subject.spa.fl_str_mv	algoritmo genético GRASP meta-optimización selección de portafolio de proyectos Optimización Metaheurísticas Meta-optimización
topic	algoritmo genético GRASP meta-optimización selección de portafolio de proyectos Optimización Metaheurísticas Meta-optimización genetic algorithms GRASP meta-optimization project portfolio selection
dc.subject.eng.fl_str_mv	genetic algorithms GRASP meta-optimization project portfolio selection
description	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.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-06-21 00:00:00 2022-06-17T20:20:45Z
dc.date.available.none.fl_str_mv	2020-06-21 00:00:00 2022-06-17T20:20:45Z
dc.date.issued.none.fl_str_mv	2020-06-21
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.eng.fl_str_mv	Journal article
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dc.relation.references.spa.fl_str_mv	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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spelling	Candia Garcia, Cristian Davidcc86e52b6b69d3d5291fb7eb79cc2819300López Castro, Luis Francisco20e1e13fcf0e8bfe9f460bd568aeed33300Jaimes Suárez, Sonia Alexandra61836a7a9da60bcca727c485486881123002020-06-21 00:00:002022-06-17T20:20:45Z2020-06-21 00:00:002022-06-17T20:20:45Z2020-06-211794-1237https://repository.eia.edu.co/handle/11190/511210.24050/reia.v17i34.13992463-0950https://doi.org/10.24050/reia.v17i34.1399Este 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.This 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.application/pdfspaFondo Editorial EIA - Universidad EIARevista EIA - 2020https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/1399algoritmo genéticoGRASPmeta-optimizaciónselección de portafolio de proyectosOptimizaciónMetaheurísticasMeta-optimizacióngenetic algorithmsGRASPmeta-optimizationproject portfolio selectionSelección óptima del portafolio de proyectos utilizando metaheurísticas de población y trayectoria meta-optimizadasOptimal Project Portfolio Selection Using Meta-Optimized Population and Trajectory-Based MetaheuristicsArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85Agarwal, 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/10.1080/00207543.2017.1306134Boryssenko, 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.2018.8609394Cetin, 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.2015.02.060Colombia 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/10.1155/2014/189164Deng, 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.2016.06.002Eshlaghy, 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.2015.067262Faezy 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.2017.276Faia, 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.2016.7849858Garcia, 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-11-2013-0245Ghayour, 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-08-2012-0085Griffith, 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.2016.10.004Instituto 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.12419INVIAS, 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.2012.102Neumüller, 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.2014.6867465Panadero, 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-zPedersen, 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/10.1007/978-1-4939-6530-4Shadkam, 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.2015.5304Yu, 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/s10479-010-0819-6https://revistas.eia.edu.co/index.php/reveia/article/download/1399/1349Núm. 34 , Año 20201834117Revista EIAPublicationOREORE.xmltext/xml2730https://repository.eia.edu.co/bitstreams/2f2f4252-436d-4786-8d9a-b28d29b50cc8/downloaddd8479fdb3238b5333f08b92d32bce55MD5111190/5112oai:repository.eia.edu.co:11190/51122023-07-25 17:21:01.187https://creativecommons.org/licenses/by-nc-nd/4.0Revista EIA - 2020metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com

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

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