Estudio paramétrico de un algoritmo genético con redes de Petri

Gonzalo Eduardo Heredia-Medina; Joselito Medina-Marin; Juan Carlos Seck-Tuoh-Mora; Norberto Hernández-Romero; Irving Barragán-Vite

doi:10.29057/icbi.v14iespecial3.18131

Autores/as

Gonzalo Eduardo Heredia-Medina Universidad Autónoma del Estado de Hidalgo https://orcid.org/0009-0008-4668-8345
Joselito Medina-Marin Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-0937-8707
Juan Carlos Seck-Tuoh-Mora Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-3678-1120
Norberto Hernández-Romero Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0001-9942-306X
Irving Barragán-Vite Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-3625-6943

DOI:

https://doi.org/10.29057/icbi.v14iespecial3.18131

Palabras clave:

Programación job-shop, Algoritmo genético, Redes de Petri, Makespan, Estudio paramétrico

Resumen

El problema de programación de tareas tipo job-shop (JSSP) es NP-difícil y requiere métodos aproximados. Este trabajo evalúa un algoritmo genético acoplado a una red de Petri temporizada adaptativa, empleada como evaluador exacto del makespan. Mediante un estudio paramétrico factorial completo 3×3 se combinaron tres tamaños de población (50, 75 y 100) y tres umbrales de reinicio por estancamiento (10, 15 y 20), sobre 18 instancias estándar de dimensión 10×10, con diez corridas independientes por configuración (1620 corridas). La calidad se midió mediante la desviación porcentual relativa (RPD) respecto a la mejor solución conocida. El tamaño de población fue el principal factor regulador de la calidad, mientras que el umbral de reinicio tuvo un efecto marginal; la mejor configuración agregada fue 100×15. El tiempo escaló de forma aproximadamente lineal con la población, lo que sugiere priorizar su incremento sobre el ajuste del umbral de reinicio.

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