[1]李雅丽,李生生,李佳森.基于自适应引导进化算法的柔性作业车间调度[J].机械与电子,2026,44(05):95-101.
　LI Yali,LI Shengsheng,LI Jiasen.Flexible Job Shop Scheduling Based on an Adaptive Guided Evolutionary Algorithm[J].Machinery & Electronics,2026,44(05):95-101.
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基于自适应引导进化算法的柔性作业车间调度()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年05期
页码:
95-101
栏目:
智能制造
出版日期:
2026-05-27

文章信息/Info

Title:
Flexible Job Shop Scheduling Based on an Adaptive Guided Evolutionary Algorithm
文章编号:
1001-2257 ( 2026 ) 05-0095-07
作者:
李雅丽 1 ; 李生生 2 ; 李佳森 3
1. 山西华澳商贸职业学院,山西 晋中 030031 ;?
2. 中铁城建集团第一工程有限公司,山西 太原 030024 ;
3. 山西工程职业学院,山西 太原 030009
Author(s):
LI Yali1 ;  LI Shengsheng2 ;  LI Jiasen3
( 1.Shanxi Hua ’ ao International Trade and Business Vocational College , Jinzhong 030031 , China ;
?2.China Railway Urban Construction Group No.1 Engineering Co. , Ltd. , Taiyuan 030024 , China ;
3.Shanxi Engineering Vocational College , Taiyuan 030009 , China )
关键词:
柔性作业车间调度; 自适应; 基因引导; 邻域搜索策略
Keywords:
flexible job shop scheduling ;  adaptive weight ;  gene guidance ;  neighborhood search strategy
分类号:
P18 ;TH165
文献标志码:
A
摘要:
针对遗传算法在求解固定权重的柔性作业车间调度时,难以平衡多目标之间的分配和局部搜索能力弱的问题,提出了自适应引导的遗传算法求解柔性作业车间调度。采用自适应策略平衡完工时间和机器负载目标之间的权重。利用遗传算法全局搜索获得的精英个体基因引导较差个体进化,提高算法收敛速度。为进一步提高子代个体的质量,引入邻域搜索策略加强个体的局部开发能力。在 15 个算例上进行仿真测试,结果表明:所提算法在最大完工时间指标上多数算例获得最优或并列最优解;与标准遗传算法( GA )相比,平均性能整体更优,并保持了可接受的波动范围。此外,相对GA ,所提算法的最大完工时间均值降低约5.51% ,体现了动态权重、优秀基因引导与邻域开发对收敛质量的提升。
Abstract:
To address the challenges of balancing multi-objective allocations with fixed weights and the inherent weakness in local search capabilities of Genetic Algorithms ( GAs ), an adaptive guided genetic algorithm is proposed to solve the problem of flexible job shop scheduling.An adaptive strategy is implemented to dynamically balance the weights between completion time and machine load targets.The genetic algorithm is used to globally search for elite individual genes , guiding the evolution of poor individuals , with an improvement of the convergence speed.Furthermore , a neighborhood search strategy is introduced to enhance their local exploitation capability and improve the quality of offspring individuals.Simulation tests conducted on 15 examples show that the proposed algorithm achieves optimal or tied optimal solutions in the majority of examples for the maximum completion time metric.Compared with the standard genetic algorithm , the proposed approach exhibits superior overall average performance , while maintaining an acceptable range of fluctuations.In addition , compared with the GA , the average maximum completion time of the proposed algorithm is reduced by about 5.51% , reflecting that the integration of dynamic weights , excellent gene guidance , and neighborhood exploitation significantly enhances convergence quality.

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备注/Memo

备注/Memo:
收稿日期: 2026-01-15
基金项目:山西省科技厅课题重点科技攻关项目( 242102321139 )
作者简介:李雅丽 ( 1989- ),女,山西长治人,硕士,讲师,研究方向为大数据分析;李生生 ( 1990- ),男,山西太原人,工程师,研究方向为智能建造。
更新日期/Last Update: 2026-08-25