Research on equipment cloudification effect evaluation in steel industry based on improved entropy weight TOPSIS

doi:10.16731/j.cnki.1671-3133.2024.08.004

Abstract

Abstract: To accurately evaluate the effects of economic,environmental protection,and capacity improvement achieved by the equipment cloudification,solve the problem of lacking evaluation methods for equipment cloudification effects,enterprises generally rely on expert experience or economic indicators to evaluate equipment cloudification effect,resulting in strong subjectivity,single dimensionality,and inaccurate evaluation. An equipment cloudification effect evaluation index system for the steel industry has been established based on three dimensions of equipment access,equipment service and overall revenue. An improved entropy weight TOPSIS method is proposed to set indicator weights more accurately,and fuzzy comprehensive evaluation methods are applied to calculate cloudification effect scores. An equipment cloudification effect evaluation system for the steel industry is constructed to provide self-evaluation for steel enterprises,and select 5 typical enterprises′ data to verified that this method can help enterprises evaluate the effect of equipment cloudification and guide enterprises to scientifically carry out equipment cloudification work.

Key words: steel industry, equipment cloudification, effect evaluation, improved entropy weight TOPSIS, evaluation model

CLC Number:

TP399

WANG Dianwei, ZHOU Chunhao, ZHOU Yang, SHI Yongchang, JIA Xiaoliang. Research on equipment cloudification effect evaluation in steel industry based on improved entropy weight TOPSIS[J]. Modern Manufacturing Engineering, 2024, 527(8): 27-34.

References

[1] MAIYAMA K M,KOUVATSOS D D,MOHAMMED B,et al.Performance Modelling and Analysis of an Open Stack IaaS Cloud Computing Platform[C]//2017 IEEE 5th International Conference on Future Internet of Things and Cloud.[S.l.]:IEEE,2017:198-205.
[2] AHUJA S P,DEVAL N.On the Performance Evaluation of IaaS Cloud Services With System-Level Benchmarks[J].International Journal of Cloud Applications & Computing,2018,8(1):80-96.
[3] SCHLUGA O,BAUER E,BICAKU A,et al.Operations Security Evaluation of IaaS-Cloud Backend for Industry 4.0[C]//8th International Conference on Cloud Computing and Services Science.[S.l.]:[s.n.],2018.
[4] 李文迪,陈华伟,伍权,等.设备上云技术研究现状与展望[J].机床与液压,2020,48(15):194-198.
[5] 邱君降,李君,成雨,等.制造业与互联网融合背景下工业设备“上云”的现状、问题与路径研究[J].制造业自动化,2018,40(10):37-41.
[6] 李君,周勇,邱君降,等.制造企业工业互联网平台应用水平与绩效评价体系构建与实践[J].计算机集成制造系统,2021,27(7):1843-1859.
[7] 李君,邱君降,窦克勤,等.基于成熟度视角的工业互联网平台评价研究[J].科技管理研究,2019,39(2):43-47.
[8] LI Jun,QIU Junjiang,ZHOU Yong,et al.Study on the reference architecture and assessment framework of industrial internet platform[J].IEEE Access,2020,(8):164950-164971.
[9] 葛秀欣,朱志斌.钢铁行业数字孪生工厂平台的研究与应用[J].冶金自动化,2021,45(6):37-45.
[10] 孙大淼.设备形态与设备管理指标体系[C]//第五届设备管理第八届设备润滑与液压学术会议论文集——《设备管理设备润滑与液压技术》.[出版地不详]:[出版者不详],2004:55-58.
[11] BRIAN S,MARY T,KARUNA P J.Framework for cloud usability[C]//Lecture Notes in Computer Science(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).Los Angeles:Springer Verlag,2015:664-671.
[12] 周平,王泽胜,殷波,等.基于层次分析法的云服务可用性评价方法[J].北京邮电大学学报,2021,44(1):79-85.DOI:10.13190/j.jbupt.2019-101.
[13] 周相兵,佘堃,马洪江.一种云服务的质量模型研究[J].小型微型计算机系统,2013,34(12):2718-2723.
[14] 《工业互联网平台建设及推广指南》和《工业互联网平台评价方法》发布[J].工业控制计算机,2018,31(8):7.
[15] 徐蔼婷.德尔菲法的应用及其难点[J].中国统计,2006(9):57-59.
[16] 郭金玉,张忠彬,孙庆云.层次分析法的研究与应用[J].中国安全科学学报,2008,18(5):148-153.
[17] 兰继斌,徐扬,霍良安,等.模糊层次分析法权重研究[J].系统工程理论与实践,2006(9):107-112.
[18] 程启月.评测指标权重确定的结构熵权法[J].系统工程理论与实践,2010,30(7):1225-1228.
[19] 王旭,谢敏,林云.TOPSIS定权的模糊综合评判法及其应用[J].统计与决策,2011(20):165-166.
[20] 姜福川,周师,吴增彤,等.基于熵权-TOPSIS法的煤矿安全投入决策分析[J].中国安全科学学报,2021,31(7):24-29.
[21] 董晓旭,何安瑞,孙文权,等.应用熵权-TOPSIS法的加热炉炉温在线设定模型[J].哈尔滨工业大学学报,2017,49(7):119-124.
[22] 姜安民,董彦辰,刘霁,等.基于改进熵权TOPSIS模型的岩质边坡稳定性评价[J].土木工程与管理学报,2019,36(4):133-139.DOI:10.13579/j.cnki.2095-0985.2019.04.021.