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

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Candia García, Cristian David
López Castro, Luis Francisco
Jaimes Suárez, Sonia Alexandra
Abstract (Spanish)
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) supervi-sados por el Instituto Nacional de Vías (INVIAS) en Colombia. En esta modalidad, cada con-cursante 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.
Abstract (English)
This article addresses the problem of project portfolio selection for the adjudication of public works audits through open merit competitions (CMA) supervised by the National Institute of Roads (INVIAS) in Colombia. In this modality, each contestant presents a unique portfolio of historical projects to quantify their experience as a controller. As an alternative to using Excel spreadsheets with limited exhaustive enumeration procedures, a meta-optimized genetic algorithm (GA) and a meta-optimized probabilistic adaptive greedy search procedure (GRASP) were evaluated for a Company case study. with 207 experience contracts in the sector. Both metaheuristics were able to find optimal evaluation scores for different test instances, however, the GA algorithm presented a consistently better performance in all evaluation instances, finding in some cases up to 10 optimal portfolios in less than 9 minutes.
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18 p.
URI: https://repositorio.escuelaing.edu.co/handle/001/1590
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AI - Centro de Investigación en Manufactura y Servicios – CIMSER
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