Algoritmo genético mejorado para la minimización de la tardanza total en un flowshop flexible con tiempos de preparación dependientes de la secuencia

Abstract

This paper considers a production environment of a flexible flowshop with anticipatory sequencedependent setup times. A genetic algorithm improved to minimize total tardiness is presented. The generation of the initial population is performed using the heuristics EDD (Earliest Due Date) and Slack neighborhoods, also considering further search in IP neighborhood to improve the performance of the proposed genetic algorithm. The results show that the genetic algorithms with initial population generated as EDD (AG_EDD) and Slack (AG_Slack) neighborhood improve the performance of the basic genetic algorithm. The AG_EDD algorithm shows better performance, the feature that remains when incorporating IP neighborhood search.