Comparación de algoritmos genéticos, colonia de abejas y colonia de hormigas para la optimización multiobjetivo de redes logísticas
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https://doi.org/10.70577/s.e.v4i3.131Palabras clave:
Algoritmos evolutivos, colonia de abejas, colonia de hormigas, logística, optimización multiobjetivo.Resumen
La optimización de redes logísticas es un problema complejo debido a la necesidad de reducir tiempos de entrega, costos operativos y riesgos de retraso en entornos dinámicos. El objetivo del estudio fue comparar el desempeño del Algoritmo Genético, la Colonia Artificial de Abejas y la Colonia de Hormigas en la optimización multiobjetivo de una red logística. Se aplicó un enfoque cuantitativo, comparativo y aplicado, mediante experimentación computacional sobre una muestra censal de 1.000 registros correspondientes a 10 camiones, utilizando regresión logística para estimar la probabilidad de retraso y tres funciones objetivo: tiempo, costo y riesgo. Los resultados evidenciaron que el Algoritmo Genético generó 17 soluciones no dominadas y redujo el objetivo agregado en 33,9% frente a una asignación aleatoria. En las soluciones balanceadas, ABC alcanzó 5.765,38 horas, USD 13.992,64 y riesgo de 34,84, mientras que ACO obtuvo 4.202,34 horas, USD 10.486,61 y riesgo de 34,94, mejorando frente al GA en 50,92 % en tiempo, 49,26% en costo y 7,17% en riesgo. Se concluyó que ACO presentó el mejor desempeño global bajo las condiciones evaluadas.
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