中文 | English

J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Research on batch scheduling of hybrid flow shop based on genetic algorithm

Download Full Text

基于遗传算法的混合流水车间批量调度研究

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Jun. 2026, Vol. 49 No. 6: 752-757

DOI: 10.3969/j.issn.1003-5060.2026.06.005

Authors

QU Xinhuai, YAO Tengjia, DING Birong, MENG Guanjun

(School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: Aiming at the hybrid flow-shop scheduling problem with lot-streaming(HFSP_LS), an improved genetic algorithm(IGA) with adaptive crossover probability and mutation probability is designed. Firstly, the mathematical model of batch scheduling in hybrid flow shop is established by minimizing the maximum completion time as the objective function. Secondly, the matrix encoding and decoding strategies for the first phase are designed according to the first come first serve(FCFS) principle. Then, the global search capability of the algorithm is enhanced by integrating the selective operation of elite retention strategy and roulette method, adaptive crossover probability and multi-fragment crossover operation, adaptive mutation probability and multi-fragment mutation operation. Finally, the effectiveness and superiority of the proposed algorithm are verified by case test and algorithm comparison.

Keywords: hybrid flow shop; batch scheduling; improved genetic algorithm(IGA); genetic operation; elite retention strategy

作者信息

屈新怀，姚腾佳，丁必荣，孟冠军

（合肥工业大学机械工程学院，安徽合肥230009）

摘要和关键词

摘要: 针对混合流水车间批量调度问题(hybrid flow-shop scheduling problem with lot-streaming, HFSPLS), 文章设计改进遗传算法(improved genetic algorithm, IGA)。以最小化最大完工时间为目标函数, 建立混合流水车间批量调度数学模型; 结合先到先服务(first come first serve, FCFS)原则设计仅针对第1阶段的矩阵编码和解码策略; 通过融合精英保留策略和轮盘赌法的选择操作、自适应交叉变异概率增强算法全局搜索能力。最终通过相关案例测试和算法比较验证了该算法的有效性和优越性。

关键词: 混合流水车间；批量调度；改进遗传算法（IGA）；遗传操作；精英保留策略

Funding

国家重点研发计划资助项目（2019YFB1705303）

Previous Next

Back to Contents