Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

• 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: 261-271
• 页数: 11页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000023

摘要

The flexible job shop scheduling problem (FJSP), which is NP-hard, widely exists in many manufacturing indus-tries. It is very hard to be solved. A multi-swarm collaborative genetic algorithm (MSCGA) based on the collaborative optimiza-tion algorithm is proposed for the FJSP. Multi-population struc-ture is used to independently evolve two sub-problems of the FJSP in the MSCGA. Good operators are adopted and designed to ensure this algorithm to achieve a good performance. Some famous FJSP benchmarks are chosen to evaluate the effective-ness of the MSCGA. The adaptability and superiority of the pro-posed method are demonstrated by comparing with other repor-ted algorithms.