Método híbrido para optimizar el Flexible Job Shop Scheduling Problem

Nayeli J. Escamilla-Serna; Juan C. Seck-Tuoh-Mora; Joselito Medina-Marín; Irving Barragan-Vite; José R. Corona-Armenta

doi:10.29057/icbi.v10iEspecial2.8651

Nayeli J. Escamilla-Serna Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-8231-8682
Juan C. Seck-Tuoh-Mora Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-3678-1120
Joselito Medina-Marín Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-0937-8707
Irving Barragan-Vite Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-3625-6943
José R. Corona-Armenta Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0001-7157-1634

DOI: https://doi.org/10.29057/icbi.v10iEspecial2.8651

Palabras clave: • Flexible Job Shop Scheduling Problem, algoritmos genéticos, escalada de colina, optimización híbrida

Resumen

Este artículo aborda la programación de tareas en el Flexible Job Shop Scheduling Problem (FJSSP). En este sistema de manufactura es necesario incrementar el número de trabajos a procesar debido a las condiciones actuales del sector industrial en donde existe un aumento en la demanda de productos, lo que conlleva a incrementar la producción. Para encontrar una programación de tareas cercana al óptimo. Se propone un método de optimización híbrida utilizando una búsqueda global basada en algoritmos genéticos (AG) que tienen buena diversificación y para la búsqueda local se aplica una escalada de colinas simple con reinicio (ECR) para mejorar cada solución. La combinación de estas metaheurísticas obtiene el equilibrio necesario para encontrar la mejor programación de tareas con el fin de minimizar el makespan como función costo. Se implementó el algoritmo propuesto en Matlab, para comprobar su eficiencia se compararon los resultados con investigaciones recientemente publicadas.

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