An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches
Xiuli Wu, Peng Junjian, Zirun Xie, Ning Zhao, Shaomin Wu
Abstract
In order to solve the flexible job shop scheduling problem with variable batches, we propose an improved multi-objective optimization algorithm, which combines the idea of inverse scheduling. First, a flexible job shop problem with the variable batches scheduling model is formulated. Second, we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method. Moreover, in order to increase the diversity of the population, two methods are developed. One is the threshold to control the neighborhood updating, and the other is the dynamic clustering algorithm to update the population. Finally, a group of experiments are carried out. The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively, and has effective performance in solving the flexible job shop scheduling problem with variable batches.