计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 6-10.doi: 10.11896/jsjkx.191000147

• 人工智能 • 上一篇    下一篇

复材车间智能排产系统研究

张伟, 于成龙   

  1. 中国航天系统科学与工程研究院 北京 100048
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 于成龙(yuchengl@163.com)
  • 作者简介:277531349@qq.com
  • 基金资助:
    国防基础科研项目(JCKY2016203B083,JCKY2017203B071,JCKY2017203C105)

Study on Intelligent Scheduling System of Composite Shop

ZHANG Wei, YU Cheng-long   

  1. China Aerospace Academy of Systems Science and Engineering,Beijing 100048,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHANG Wei,born in 1983,master,engineer.His main research interests include production process control technology.
    YU Cheng-long,born in 1979,Ph.D,se-nior engineer.His main research inte-rests include intelligent manufacturing research,planning and application.
  • Supported by:
    This work was supported by the Defense Industrial Technology Development Program(JCKY2016203B083,JCKY2017203B071,JCKY2017203C105).

摘要: 为解决航天复材车间以经验为主的排产实时性和实用性不够的问题,提出了智能排产系统并讨论了智能排产系统的体系架构和功能组成,研究了基于感知信息的排产计算方法,开发了航天复材车间智能排产原型系统并进行了校验。该系统能够根据生产实时数据和排产知识,自动判断并进行资源优化配置和工序工时计算,并根据生产扰动情况执行重排产及输出排产方案,为企业排产方案的制定提供了依据,并为后续工程化智能排产系统的开发奠定了基础。

关键词: 复材车间, 工时计算, 排产系统, 智能, 资源配置

Abstract: To solve the problem of insufficient real-time and practicability of experience-based production scheduling in aerospace composite workshop,an aerospace intelligent scheduling system is proposed,and the architecture and function composition of the system are discussed.Furthermore,the calculating method based on perceptual information is studied,and the prototype system of aerospace composite workshop is developed and verified.According to the production real-time data and the scheduling know-ledge,the system can automatically judge and optimize resource allocation,calculate man-hours of each process step,and rearrange and output the scheduling plan according to the production disturbances.It provides a basis for the formulation of the enterprise scheduling program and lays the foundation for the development of the subsequent engineering intelligent scheduling system.

Key words: Composite shop, Intelligent, Man-hour calculating, Resource allocating, Scheduling system

中图分类号: 

  • TH391
[1] LIU Y H,MA J,MOU J H,et al.Dynamic sequencing of mix-model assembly line oriented to rush orders[J].Computer Integrated Manufacturing System,2017,23(12):2647-2656.
[2] KU W Y,BECK J C.Mixed integer programming models for job shop scheduling:A computational analysis[J].Computers & Operations Research,2016(73):165-173.
[3] ZHANG Y F,YANG T,WANG J Q,et al.Intelligent navigation of assembly activities based on real-time manufacturing information[J].Computer Integrated Manufacturing System,2014,20(1):28-36.
[4] FAN H L,XIONG H G,JIANG G Z,et al.Survey of dispatching rules for dynamic Job-Shop scheduling problem[J].Journal of Computer Applications,2016,33(3):648-653.
[5] FRAMINA J M,PEREZ-GONZALEZ P.New approximate algorithms for the customer order scheduling problem with total completion time objective[J].Computers & Operations Research,2017(78):181-192.
[6] AHMADI E,ZANDIEH M,FARROKH M,et al.A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms[J].Computers & Operations Research,2016(73):56-66.
[7] WANG L,DENG J,WANG S Y.Survey on optimization algorithms for distributed shop scheduling[J].Control and Decision,2016,31(1):1-11.
[8] ZAHMANI M,ATMANI B.A data mining based dispatchingrules selection system for the job shop scheduling problem[J].Journal of Advanced Manufacturing Systems,2019,18(1):35-56.
[9] SHOKOUHI E.Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints[J].Journal Production & Manufacturing Research,2018,69(1):61-89.
[10] BÜRG Y,REINHAR D.A neighborhood for complex job shopscheduling problems with regular objectives[J].Journal of Scheduling,2017,20(4):391-422.
[11] SUN Q Q,DING M.Research on intelligent service scheduling scheduling in aviation compound production workshop[J].Intelligent Manufacturing,2017(12):39-43.
[12] WANG Q B,ZHANG W X,WANG B L,et al.Research on Agent-based Hybrid Flow Shop Dynamic Scheduling Problem[J].Journal of Computer Applications,2017,37(10):2991-2998.
[13] MIRKO K,JENS E,MICHAEL F,et al.A data-driven simulation-based optimisation approach for adaptive scheduling and control of dynamic manufacturing systems[J].Advanced Materials Research,2016(1140):449-456.
[14] BARENJI A V,BARENJI R V,ROUDI D,et al.A dynamic multi-agent-based scheduling approach for SMEs[J].The International Journal of Advanced Manufacturing Technology,2017(89):3123-3137.
[15] NASIRI M M,YAZDANPARAST R,JOLAI F.A simulation optimisation approach for real-time scheduling in an open shop environment using a composite dispatching rule[J].International Journal of Computer Integrated Manufacturing,2017,30(12):1239-1252.
[1] 熊丽琴, 曹雷, 赖俊, 陈希亮.
基于值分解的多智能体深度强化学习综述
Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization
计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112
[2] 刘兴光, 周力, 刘琰, 张晓瀛, 谭翔, 魏急波.
基于边缘智能的频谱地图构建与分发方法
Construction and Distribution Method of REM Based on Edge Intelligence
计算机科学, 2022, 49(9): 236-241. https://doi.org/10.11896/jsjkx.220400148
[3] 胡玉姣, 贾庆民, 孙庆爽, 谢人超, 黄韬.
融智算力网络及其功能架构
Functional Architecture to Intelligent Computing Power Network
计算机科学, 2022, 49(9): 249-259. https://doi.org/10.11896/jsjkx.220500222
[4] 宁晗阳, 马苗, 杨波, 刘士昌.
密码学智能化研究进展与分析
Research Progress and Analysis on Intelligent Cryptology
计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[5] 王子凯, 朱健, 张伯钧, 胡凯.
区块链与智能合约并行方法研究与实现
Research and Implementation of Parallel Method in Blockchain and Smart Contract
计算机科学, 2022, 49(9): 312-317. https://doi.org/10.11896/jsjkx.210800102
[6] 袁唯淋, 罗俊仁, 陆丽娜, 陈佳星, 张万鹏, 陈璟.
智能博弈对抗方法:博弈论与强化学习综合视角对比分析
Methods in Adversarial Intelligent Game:A Holistic Comparative Analysis from Perspective of Game Theory and Reinforcement Learning
计算机科学, 2022, 49(8): 191-204. https://doi.org/10.11896/jsjkx.220200174
[7] 史殿习, 赵琛然, 张耀文, 杨绍武, 张拥军.
基于多智能体强化学习的端到端合作的自适应奖励方法
Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning
计算机科学, 2022, 49(8): 247-256. https://doi.org/10.11896/jsjkx.210700100
[8] 黄松, 杜金虎, 王兴亚, 孙金磊.
以太坊智能合约模糊测试技术研究综述
Survey of Ethereum Smart Contract Fuzzing Technology Research
计算机科学, 2022, 49(8): 294-305. https://doi.org/10.11896/jsjkx.220500069
[9] 傅丽玉, 陆歌皓, 吴义明, 罗娅玲.
区块链技术的研究及其发展综述
Overview of Research and Development of Blockchain Technology
计算机科学, 2022, 49(6A): 447-461. https://doi.org/10.11896/jsjkx.210600214
[10] 高健博, 张家硕, 李青山, 陈钟.
RegLang:一种面向监管的智能合约编程语言
RegLang:A Smart Contract Programming Language for Regulation
计算机科学, 2022, 49(6A): 462-468. https://doi.org/10.11896/jsjkx.210700016
[11] 卫宏儒, 李思月, 郭涌浩.
基于智能合约的秘密重建协议
Secret Reconstruction Protocol Based on Smart Contract
计算机科学, 2022, 49(6A): 469-473. https://doi.org/10.11896/jsjkx.210700033
[12] 李荪, 曹峰.
智能语音技术端到端框架模型分析和趋势研究
Analysis and Trend Research of End-to-End Framework Model of Intelligent Speech Technology
计算机科学, 2022, 49(6A): 331-336. https://doi.org/10.11896/jsjkx.210500180
[13] 王钰珏, 梁宇豪, 王素琴, 朱登明, 石敏.
机械零件加工工艺本体库构建
Construction of Ontology Library for Machining Process of Mechanical Parts
计算机科学, 2022, 49(6A): 661-666. https://doi.org/10.11896/jsjkx.210800013
[14] 谢万城, 李斌, 代玥玥.
空中智能反射面辅助边缘计算中基于PPO的任务卸载方案
PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing
计算机科学, 2022, 49(6): 3-11. https://doi.org/10.11896/jsjkx.220100249
[15] 周天清, 岳亚莉.
超密集物联网络中多任务多步计算卸载算法研究
Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks
计算机科学, 2022, 49(6): 12-18. https://doi.org/10.11896/jsjkx.211200147
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!