Data-driven manufacturing sustainability assessment

被引:0
作者
Zhang X. [1 ,2 ]
Chen J. [1 ,2 ]
Wang Y. [1 ,2 ]
Zhang H. [1 ,2 ]
Jiang Z. [1 ,2 ]
Cai W. [3 ,4 ]
机构
[1] Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan
[2] Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan
[3] College of Engineering and Technology, Southwest University, Chongqing
[4] Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University
来源
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 08期
基金
中国国家自然科学基金;
关键词
data driven; manufacturing industry; multi-objective optimization; principal component analysis; sustainable assessment;
D O I
10.13196/j.cims.2022.08.005
中图分类号
学科分类号
摘要
Aiming at the multi-attribute characteristics of manufacturing data, the multi-attribute sustainability evaluation index system of manufacturing industry was constructed. The Principal Component Analysis (PCA) was used to evaluate the sustainable performance score of each manufacturing. Then Back Propagation-Dematel Analytic Network Process (BP-DANP) method was used to determine the comprehensive weightof data property, Influence Strength Network Relationship Map (ISNRM) and Critical Influence Strength Route (CISR). The gap between the current sustainable performance and the expected of the manufacturing industry was evaluated by using Multi-objective Optimization based on Ratio Analysis method (MOORA), and the optimizing direction was proposed. The results showed that the computer, communication and other electronic equipment manufacturing industry had the highest score under the economic and technological indicators, while the metal products, machinery and equipment repair industry had the highest score under the environmental indicators. The comprehensive analysis showed that the instrument manufacturing industry had the largest sustainable performance, and the instrument manufacturing industry could be committed to improving the ratio of fixed assets investment to improve the sustainable performance. © 2022 CIMS. All rights reserved.
引用
收藏
页码:2329 / 2342
页数:13
相关论文
共 35 条
[1]  
SONG Min, LI Shiping, WANG Shaohui, Evaluation of sustainable development of Shaanxi energy industry based on AHP, Forum on Statistics and Information, 24, 7, pp. 79-81, (2009)
[2]  
SAYYADI R, AWASTHI A., An integrated approach based on system dynamics and ANP for evaluating sustainable transportation policies, International Journal of Systems Science Operations &. Logistics, 7, 2, pp. 182-191, (2020)
[3]  
HAN Yuanwen, BAO Xueying, Study on evaluation model of dust control in railway station building construction based on DEMATEL-ANP method, Highway Engineering, 44, 6, pp. 46-50, (2019)
[4]  
ZHAN Yi, Supplier selection of prefabricated housing components based on DEMATEL method [D], (2017)
[5]  
GONG Xiaomin, GENG Xiuli, SUN Shaorong, Multi attribute group decision making method based on binary semantics DEMATEL and DEA, Computer Integrated Manufacturing Systems, 22, 8, pp. 1992-2000, (2016)
[6]  
LI Jinpeng, LI Wu, YUE Chaoyuan, Et al., TOPSIS method for ordinal multiple attribute decision making with incomplete weight information [J], Computer Integrated Manufacturing Systems, 19, 6, pp. 1408-1413, (2013)
[7]  
ZENG Jianqiu, SUN Chu, Identification of influencing factors of tutor guidance effect of graduate students based on DANP [J], Journal of Beijing University of Posts and Telecommunications, 21, 2, pp. 96-106, (2019)
[8]  
ASIF A, TABASAM R., Hesitant fuzzy best-worst multi-criteria decision-making method and its applications, International Journal of Intelligent Systems, 34, 8, pp. 1953-1967, (2019)
[9]  
GOLMOHAMMAD D., Neural network application for fuzzy multi-criteria decision making problems, International Journal of Production Economics, 131, 2, pp. 490-504, (2011)
[10]  
CHEN J, LIN S., An interactive neural network-based approach for solving multiple criteria decision-making problems, Decision Support Systems, 36, 2, pp. 137-146, (2004)