Algoritmo basado en aprendizaje probabilístico para la optimización del makespan en el problema de programación en taller de flujo

Monica Paola Gonzalez Trinidad; Citlali Acosta Acosta; Joselito Medina Martín; Maria Guadalupe Serna Díaz; Gustavo Erick Anaya Fuentes

doi:10.29057/icbi.v14iespecial3.18078

Autores/as

Monica Paola Gonzalez Trinidad Universidad Autónoma del Estado de Hidalgo https://orcid.org/0009-0001-6095-2850
Citlali Acosta Acosta Universidad Autónoma del Estado de Hidalgo https://orcid.org/0009-0008-9528-0020
Joselito Medina Martín Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-0937-8707
Maria Guadalupe Serna Díaz Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-2846-6908
Gustavo Erick Anaya Fuentes Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-3708-6763

DOI:

https://doi.org/10.29057/icbi.v14iespecial3.18078

Palabras clave:

FSSP, Algoritmos metaheurísticos, Aprendizaje adaptativo

Resumen

En este artículo se presenta la propuesta de un algoritmo metaheurístico basado en aprendizaje probabilístico adaptativo para la búsqueda de permutaciones con el propósito de obtener el valor mínimo del makespan en el problema de programación en taller de flujo (Flow Shop Scheduling Problem, FSSP). A diferencia de los métodos basados en poblaciones, como la optimización por enjambre de partículas o los algoritmos genéticos, el método propuesto emplea un mecanismo de aprendizaje probabilístico sobre las asignaciones en la posición de los trabajos, combinado con una perturbación adaptativa y una búsqueda local selectiva. La estructura del algoritmo se enfoca en una única solución, permitiendo generar soluciones competitivas mediante un mecanismo de aprendizaje probabilístico adaptativo. Los resultados experimentales muestran que el algoritmo propuesto alcanzó una eficiencia global del 96 % respecto a los mejores valores de makespan obtenidos, logró el valor óptimo en diversas instancias y mantuvo un desempeño competitivo frente a los algoritmos evaluados para la minimización del makespan en el Flow Shop Scheduling Problem.

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