Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 167-171.

• Intelligent Computing • Previous Articles     Next Articles

Research on Muti-AGV Sechduling Algorithm Based on Improved Hybrid PSO-GA for FMS

YUE Xiao-han, XU Xiao-jian, WANG Xi-bo   

  1. School of Information,Shenyang University of Technology,Shenyang 110000,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: The antomated guided vehicle(AGV) is often used to transport materials for improving prodiction efficiency in manufacturing facility or a warehouse.AGV scheduling not only needs to consider the AGV task assignment problem,but also needs to consider the time spent for each operation and the running time of the car.Compared with single-objective optimization scheduling algorithm,multi-objective optimization requires a more complex model to support.This model optimizes the two dimensions of minimizing the completion time and scheduling the minimum number of AGVs considering the power status of the AGV.This paper presented an improved hybrid particle swarm optimization and genetic algorithm (PSO-GA) to optimize the model.Compared with the GA or PSO algorithm,the proposed algorithm has significant optimization effect.Compared to PSO-GA hybrid algorithm,it is further improved in the running time.

Key words: Autimated guided vehicle, Fuzzy mixing PSO-GA, Genetic algorithm, Multi-objective opimization, Particle swarm optimization, Scheduling

CLC Number: 

  • TP391.7
[1]KOO P H,JANG J J,SUH J D.Vehicle dispatching for highfily loaded semiconductor production bottle-neck machines first [J].International Journal of Flexifible Manufacturing System,2005,17(1):23-38.
[2]樊树海,陈金龙,曹霞,等.顺序矩阵扩展研究及其在流水车间布置中的应用 [J].工业工程与管理,2008,13(6):51-54.
[3]AYDEMIR E,KORUCA H I.A new production scheduling module using priority-rule based genetic glgorithmnm[J].International Journal of Simulation Modeling,2015,14(3):450-462.
[4]李雪芹,丰伟.车辆优化调度的遗传算法求解[J].铁道运输与经济,2006,29(1):73-75.
[5]柳赛男,柯映林.自动化仓库系统 AGV 小车优化调度方法[J].组合机床与自动化加工技术,2008(6):23-25.
[6]CAI Q,TANG D,ZHENG K,et al.Multi-AGV scheduling optimization based on neuro-endocrine coordination mechanism[J].Inrternational Journal on Smart Sensing and Intelligent Systems,2014,7(4):1613-1630.
[7]杨立熙,余慧慧.考虑运输时间的柔性作业车间调度问题研究 [J].武汉理工大学学报,2017,39(1):608-613.
[8]KRISHNAN M,KARTHIKEYAN T,CHINNUSAMY T R,et al.An Evolutionary Hybrid Algorithm for Layout Planning in Flexible Manufacturing System[J].Advanced Materials Research,2014,984-985:444-451.
[9]CHANG C D J,LIU S H J.AGV Control Using PSO for ANFIS-PID Controller Parameters Tuning[C]∥International Conference on Electric Information & Control Engineering.2012:176-179.
[10]汤晏安,谷宝慧.改进PSO在AGV系统路径优化调度中的应用研究[J].计算机工程应用,2016,52(3):261-265.
[11]徐云琴,叶春明,曹磊.含有AGV的柔性车间调度优化研究[J].计算机应用研究,2017,35(11).
[12]MOUSAVI M,YAP H J,MUSA S N,et al.Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization[J].Plos One,2017,12(3):e0169817.
[13]REDDY B S P,RAO C S P.Flexible manufacturing system mo-delling and performance evaluation using Automod[J].International Journal of Simulation Modelling,2011,10(2):78-90.
[14]DE K,LI J,WANG W C.Research on flexible manufacturing system real-time scheduling optimization[J].Machine Tool & Hydraulics,2015(18):44-47.
[15]Egemin Automation Inc.Battery charging systems for automated guided vehicles[OL].http://www.egeminusa.com/pages/agvs/agvscharging.html.
[16]TALEIZADEH A A,NIAKI S T A,ARYANEZHAD M B,et al.A hybrid method of fuzzy simulation and genetic algorithm to optimize constrained inventory control systems with stochastic replenishments and fuzzy demand[J].Information Sciences,2013,220:425-441.
[17]TASGETIREN M F,SEVKLI M,LIANG Y C,et al.Particle swarm optimization algorithm for single machine total weighted tardiness problem[C]∥Congress on Evolutionary Computation(CEC 2004).2004:1412-1419.
[1] ZHAO Dong-mei, WU Ya-xing, ZHANG Hong-bin. Network Security Situation Prediction Based on IPSO-BiLSTM [J]. Computer Science, 2022, 49(7): 357-362.
[2] YANG Hao-xiong, GAO Jing, SHAO En-lu. Vehicle Routing Problem with Time Window of Takeaway Food ConsideringOne-order-multi-product Order Delivery [J]. Computer Science, 2022, 49(6A): 191-198.
[3] LIU Zhang-hui, ZHENG Hong-qiang, ZHANG Jian-shan, CHEN Zhe-yi. Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems [J]. Computer Science, 2022, 49(6A): 619-627.
[4] TIAN Zhen-zhen, JIANG Wei, ZHENG Bing-xu, MENG Li-min. Load Balancing Optimization Scheduling Algorithm Based on Server Cluster [J]. Computer Science, 2022, 49(6A): 639-644.
[5] QIU Xu, BIAN Hao-bu, WU Ming-xiao, ZHU Xiao-rong. Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication [J]. Computer Science, 2022, 49(6): 25-31.
[6] ZHANG Jie, TANG Qiang, LIU Shuo-han, CAO Yue, ZHAO Wei, LIU Tao, XIE Shi-ming. Priority Based EV Charging Management Under Service Reservation in Smart Grid [J]. Computer Science, 2022, 49(6): 55-65.
[7] LI Xiao-dong, YU Zhi-yong, HUANG Fang-wan, ZHU Wei-ping, TU Chun-yu, ZHENG Wei-nan. Participant Selection Strategies Based on Crowd Sensing for River Environmental Monitoring [J]. Computer Science, 2022, 49(5): 371-379.
[8] LIU Peng, LIU Bo, ZHOU Na-qin, PENG Xin-yi, LIN Wei-wei. Survey of Hybrid Cloud Workflow Scheduling [J]. Computer Science, 2022, 49(5): 235-243.
[9] TIAN Bing-chuan, TIAN Chen, ZHOU Yu-hang, CHEN Gui-hai, DOU Wan-chun. Reducing Head-of-Line Blocking on Network in Hadoop Clusters [J]. Computer Science, 2022, 49(3): 11-22.
[10] LIN Chao-wei, LIN Bing, CHEN Xing. Study on Scientific Workflow Scheduling Based on Fuzzy Theory Under Edge Environment [J]. Computer Science, 2022, 49(2): 312-320.
[11] TAN Shuang-jie, LIN Bao-jun, LIU Ying-chun, ZHAO Shuai. Load Scheduling Algorithm for Distributed On-board RTs System Based on Machine Learning [J]. Computer Science, 2022, 49(2): 336-341.
[12] SHEN Biao, SHEN Li-wei, LI Yi. Dynamic Task Scheduling Method for Space Crowdsourcing [J]. Computer Science, 2022, 49(2): 231-240.
[13] QU Li-cheng, LYU Jiao, QU Yi-hua, WANG Hai-fei. Intelligent Assignment and Positioning Algorithm of Moving Target Based on Fuzzy Neural Network [J]. Computer Science, 2021, 48(8): 246-252.
[14] YANG Lin, WANG Yong-jie, ZHANG Jun. FAWA:A Negative Feedback Dynamic Scheduling Algorithm for Heterogeneous Executor [J]. Computer Science, 2021, 48(8): 284-290.
[15] WU Shan-jie, WANG Xin. Prediction of Tectonic Coal Thickness Based on AGA-DBSCAN Optimized RBF Neural Networks [J]. Computer Science, 2021, 48(7): 308-315.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!