[1]陶方平,周德强,奚 青,等.基于自适应遗传算法的 AGV 与加工机器集成调度[J].机械与电子,2025,(05):55-63.
　TAO Fangping,ZHOU Deqiang,XI Qing,et al.Integration Scheduling of AGV and Processing Machinery Based on Adaptive Genetic Algorithm[J].Machinery & Electronics,2025,(05):55-63.
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基于自适应遗传算法的 AGV 与加工机器集成调度()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年05期
页码:
55-63
栏目:
智能制造
出版日期:
2025-05-23

文章信息/Info

Title:
Integration Scheduling of AGV and Processing Machinery Based on Adaptive Genetic Algorithm
文章编号:
1001-2257 ( 2025 ) 05-0055-09
作者:
陶方平 1 ; 周德强 1 ; 2 ; 奚 青 3 ; 陈曲燕 3
1. 江南大学智能制造学院,江苏 无锡 214122 ;
?2. 江苏省食品先进制造装备技术重点实验室,江苏 无锡 214122 ;?
3. 无锡弘宜智能科技有限公司,江苏 无锡 214174
Author(s):
TAO Fangping1 ;  ZHOU Deqiang1 ;  2 ;  XI Qing3 ;  CHEN Quyan3
( 1.College of Mechanical Engineering , Jiangnan University , Wuxi 214122 , China ;
2.Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology , Wuxi 214122 , China ;
3.Wuxi Hongyi Intelligent Technology Co. , Ltd. , Wuxi 214174 , China )
关键词:
柔性作业车间; 自动化引导车; 遗传算法; 集成调度; 路径规划
Keywords:
flexible job shop ;  AGV ;  genetic algorithm ;  integrated scheduling ;  path planning
分类号:
TP273
文献标志码:
A
摘要:
针对柔性作业车间中自动化引导车( AGV )加工机器集成调度问题,以最大加工时间最小化为目标,提出一种融合时间窗与 Dijkstra 算法的自适应邻域搜索遗传算法。首先,建立了 AGV 与加工机器集成调度模型。其次,为了避免潜力个体被淘汰,提出一种基于个体编码相似度的选择策略;在此基础上,根据个体差异与迭代次数,设计了自适应个体的交叉策略与交叉概率;根据个体质量设计自适应个体变异概率;为了检测并跳出局部最优解,提出一种基于种群停滞程度的自适应邻域搜索方法;引入了 AGV 优先级判断与冲突类型相结合的 AGV 避让策略。最后将时间窗与 Dijkstra 算法嵌入到自适应遗传算法的解码过程,结合避让策略,解决 AGV 的路径冲突问题。通过多个算例的分析与对比,验证了所提算法的有效性与优越性。
Abstract:
Addressing the integrated scheduling challenges of automated guided vehicles ( AGVs ) and processing machinery within flexible job shops , this study proposes a novel adaptive neighborhood search genetic algorithm that integrates time windows and the Dijkstra algorithm , aiming to minimize the maximum processing time.Initially , a comprehensive scheduling model for AGVs and processing machinery is established.To safeguard potential high-performing individuals from elimination , a selection strategy based on the similarity of individual encoding is devised ; building upon this , an adaptive individual crossing strategy and probability are formulated based on the variance among individuals and the number of iterations ; mutation probabilities are specifically designed by classifying the population into superior and inferior groups ; an adaptive neighborhood search method is introduced to detect and overcome local optima , predicated on the extent of population stagnation.Furthermore , time windows and the Dijkstra algorithm are seamlessly integrated into the decoding process of the adaptive genetic algorithm , incorporating an evasion strategy to mitigate path conflicts among AGVs.The efficacy and superiority of the algorithm are demonstrated through extensive case analyses and comparative evaluations.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2024-07-12
基金项目:无锡市科技发展资金项目( G20222014 )
作者简介:陶方平 ( 1998- ),男,安徽池州人,硕士研究生,研究方向为工厂智能调度;周德强 ( 1979- ),男,博士,副教授,研究方向为多传感器融合,通信作者, E-mail : zhoudeqiang@jiangnan.edu.cn 。
更新日期/Last Update: 2025-06-13