Optimal Project Portfolio Selection Using Meta-Optimized Population and Trajectory-Based Metaheuristics

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

Published 2020-11-15

Superficie de respuesta estimada en función de los parámetros del GA para la última iteración del meta- optimizador.
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Vol. 17 No. 34 (2020)
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Optimal Project Portfolio Selection Using Meta-Optimized Population and Trajectory-Based Metaheuristics. (2020). Revista EIA, 17(34), 1-18. https://doi.org/10.24050/reia.v17i34.1399
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https://doi.org/10.24050/reia.v17i34.1399
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Cristian David Candia Garcia
Escuela Colombiana de Ingeniería Julio Garavito
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Luis Francisco López Castro
Escuela Colombiana de Ingeniería Julio Garavito
Sonia Alexandra Jaimes Suárez
Escuela Colombiana de Ingeniería Julio Garavito
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Cristian David Candia Garcia,

Estudiante de Maestria en Ingeniería Industrial de la Escuela Colombiana de Ingeniería Julio Garavito. Consultor en analítica de datos en IQuartil SAS.

Luis Francisco López Castro,

Ingeniero Industrial de la Escuela Colombiana de Ingeniería Julio Garavito, Máster en Diseño y Gestión de Procesos de la Universidad de la Sabana. Experiencia académica extensa como profesor del progama de Ingeniería Industrial de la Escuela Colombiana de Ingeniería Julio Garavito e Investigador en las áreas de ingeniería de producción, algoritmos evolutivos, simulación y optimización de operaciones.

Sonia Alexandra Jaimes Suárez,

Máster en Ingeniería industrial con énfasis en Optimización y Logística de la Pontificia Universidad Javeriana de Bogotá, Especialista en Economía para Ingenieros e Ingeniera Industrial de la Escuela Colombiana de Ingeniería Julio Garavito. En la Escuela es Directora de la Maestría de Ingeniería Industrial y del Centro de Estudios de Optimización, así como Coordinadora del Énfasis en Logística de la Maestría en Ingeniería Industrial.

Profesora asistente en pregrado y posgrado e investigadora del Centro de Investigaciones en Manufactura y Servicios – CIMSER en la Escuela Colombiana de Ingeniería Julio Garavito.

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.

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