An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: INTELLIGENT OPTIMIZATION AND SCHEDULING
• 页码范围: 272-285
• 页数: 14页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000024

摘要

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 in-verse scheduling. First, a flexible job shop problem with the vari-able batches scheduling model is formulated. Second, we pro-pose 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 dy-namic 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 per-formance in solving the flexible job shop scheduling problem with variable batches.