Evaluation of Node Importance in Collaborative Network of Traditional Manufacturing Enterprises Based on Multiple Attribute Decision Making
DOI:
https://doi.org/10.31181/dmame7220241018Keywords:
Traditional manufacturing enterprises, Multiple attribute decision making, Collaborative networks, Complex networks, Coefficient of variation method, TOPSISAbstract
The construction and operation of collaborative production networks based on multi-subject collaboration is an important path and means for enterprises to adapt to personalized, diversified, and differentiated market demand. It is of great practical significance to identify the key collaborative subjects in the collaborative network and protect and maintain them to ensure its normal operation. To identify the key collaborative subjects in the collaborative network of traditional manufacturing enterprises, this paper proposes a method for identifying and evaluating the importance of nodes in traditional manufacturing enterprise collaborative networks. Firstly, the method uses four parameters, degree centrality, betweenness centrality, closeness centrality, and subgraph centrality, as node importance evaluation indexes, based on complex network theory. Secondly, the coefficient of variation method (CVM) is used to calculate the weights of evaluation indexes. The Multiple Attribute Decision Making (MADM) based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is then used to comprehensively evaluate node importance and identify key nodes (key collaborative subjects) in the network. Finally, the proposed method's effectiveness, rationality, and scientific nature are verified by using the measurement index of network connectivity in combination with specific enterprise cases. The results show that the failure of key nodes has a more significant impact on network connectivity. Therefore, the node importance evaluation method based on Multiple Attribute Decision Making has better performance. It helps traditional manufacturing enterprises to focus on the protection and maintenance of the key collaborative subjects when coping with the competitive environment of the external market and provides a valuable reference for the normal operation of collaborative network organizations.
Downloads
References
Alnsour, A. S., Sumadi, M. A., Shrydeh, N., Kanaan, O. A., Harb, L., & Abedalfattah, M. (2023). Industry 4.0 framework for sustainable manufacturing sector in Jordanian rural areas. International Journal of Sustainable Development and Planning, 18(5), 1523-1534. https://doi.org/10.18280/ijsdp.180523
Dabic-Miletic, S. (2023). Advanced technologies in smart factories: A cornerstone of industry 4.0. Journal of Industrial Intelligence, 1(3), 148-157. https://doi.org/10.56578/jii010302
Huang, J. (2020). An evaluation model for green manufacturing quality of children’s furniture based on artificial intelligence. International Journal of Design & Nature and Ecodynamics, 15(6), 921-930. https://doi.org/10.18280/ijdne.150618
Marchenko, O., Guk, O., Borutska, Y., Pacheva, N., & Zaichenko, V. (2023). Ensuring sustainable development of the enterprise during the transition to industry 5.0. International Journal of Sustainable Development and Planning, 18(4), 1149-1154. https://doi.org/10.18280/ijsdp.180418
Okokpujie, I. P., Tartibu, L. K., & Omietimi, B. H. (2023). Improving the maintainability and reliability in Nigerian Industry 4.0: Its challenges and the way forward from the manufacturing sector. International Journal of Sustainable Development and Planning, 18(8), 2489-2502. https://doi.org/10.18280/ijsdp.180820
Qi, Y. D., & Xiao, X. (2020). Transform of enterprise management in the era of digital economy. Journal of Management World, 36(6),135-152. https://doi.org/10.19744/j.cnki.11-1235/f.2020.0091
Stojanović, D., Joković, J., Tomašević, I., Simeunović, B., & Slović, D. (2023). Algorithmic approach for the confluence of lean methodology and industry 4.0 technologies: Challenges, benefits, and practical applications. Journal of Industrial Intelligence, 1(2), 125-135. https://doi.org/10.56578/jii010205
Jing, S. W., Feng, Y., Yan, J. A., & Niu, Z. W. (2022). From Manufacturing to Intelligent Manufacturing: How to Implement Lean Digitalization in Traditional Manufacturing Enterprises under the Perspective of Heterogeneous Property Rights. China Science and Technology Forum, 2022(8), 77-88. https://doi.org/10.13580/j.cnki.fstc.2022.08.010
Camarinha-Matos, L. M., Afsarmanesh, H., Galeano, N., & Molina, A. (2009). Collaborative networked organizations–Concepts and practice in manufacturing enterprises. Computers & Industrial Engineering, 57(1), 46-60. https://doi.org/10.1016/j.cie.2008.11.024
Lewis, J. D. (2002). Partnerships for profit: Structuring and managing strategic alliances. Simon and Schuster. https://doi.org/10.1016/0024-6301(93)90184-H
Wu, W. H., Zhao, K., & Zhang, A. M. (2021). A research on acting path of corporative innovation risk on innovation performance of firms. Journal of Research Management, 42(05), 124-132. https://doi.org/10.19571/j.cnki.1000-2995.2021.05.014
Erdős, P., & Renyi, A. (1959). On random graphs. Journal of Publications Mathematica, 6, 290-297.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small world’ networks. Nature, 393(6684),440-442. https://doi.org/10.1038/30918
Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512. https://doi.org/10.1126/science.286.5439.509
Chen, L., & Su, S. (2022). Optimization of the trust propagation on supply chain network based on blockchain plus. Journal of Intelligent Management Decision, 1(1), 17-27. https://doi.org/10.56578/jimd010103
Su, Y., & Cao, J. (2022). Structure and influencing factors of cooperative innovation network for new energy automobile. Science Research, 40(06), 1128-1142. https://doi.org/10.16192/j.cnki.1003-2053.20211112.006
Wen, S. P. (2023). Spatial coupling of mass transit networks and business centers in China's megacities: A complex network theory approach. Journal of Urban Development and Management, 2(2), 57-68. https://doi.org/10.56578/judm020201
Yao, L., Wang, X., & Duan, Y. Q. (2021). Analysis on the structure of multi-level government response information collaboration network. Intelligence Theory and Practice, 44(09), 114-121. https://doi.org/10.16353/j.cnki.1000-7490.2021.09.016
Sheikh, F. A., Wu, X. B., Zhang, Y. L., Wang, D. T., & Xiao, X. (2023). Network Characteristics if Innovation Ecosystem: Knowledge Collaboration and Enterprise Innovation. Journal of Science, Technology and Society, 28(3), 488-510. https://doi.org/10.1177/09717218231161216
Al-Omoush, K. S., de Lucas, A., & del Val, M. T. (2023). The role of e-supply chain collaboration in collaborative innovation and value-co creation. Journal of Business Research, 158, 113647. https://doi.org/10.1016/j.jbusres.2023.113647
Zhang, F., Yang, Y., Bao, B. F., Jia, J. G., & Wang, J. T. (2012). System vulnerability analysis of collaborative production networked organizations. Computer Integrated Manufacturing Systems, 18(5), 1077-1086. https://doi.org/10.13196/j.cims.2012.05.183.zhangf.029
Yu, G. D., Yang, Y., Li, F., & Zhang, X. F. (2014). Analysis and optimization on robustness of customer collaborative product innovation systems. Computer Integrated Manufacturing Systems, 20(12), 2926-2934. https://doi.org/10.13196/j.cims.2014.12.002
Wang, J. Z., & Chen, H. Z. (2021). A complex network-based risk propagation model for complex product supply chains. Statistics and Decision Making, 37(4),176-180. https://doi.org/10.13546/j.cnki.tjyjc.2021.04.038
Chen, J., Zhang., & Liu, L. (2021). Vulnerability analysis of multimodal transport networks based on complex network theory. Journal of Southeast University, 37(2),209-215. https://doi.org/10.3969/j.issn.1003-7985. 2021.02.011
Azadegan, A., & Dooley, K. (2021). A typology of supply network resilience strategies: complex collaborations in a complex world. Journal of Supply Chain Management, 57(1), 17-26. https://doi.org/10.1111/jscm.12256
Ma, F., Ao, Y. Y., Wang, X. J., He, H. N., Liu, Q., Yang, D. T., & Gou, H. Y. (2023). Assessing and enhancing urban road network resilience under rainstorm waterlogging disasters. Transportation Research Part D: Transport and Environment, 123, 103928. https://doi.org/10.1016/j.trd.2023.103928
Wang, S. L., Guo, Z. Y., Huang, X. D., & Zhang, J. H. (2024). A three-stage model of quantifying and analyzing power network resilience based on network theory. Reliability Engineering & System Safty, 241, 109681. https://doi.org/10.1016/j.ress.2023.109681
Li, W. F., & Fu, X. W. (2015). Survey on Invulnerability wireless sensor networks. Journal of Computing, 38(03),625-647. https://doi.org/10.3724/SP.J.1016.2015.00625
An, C. Q., Liu, Y. J., Wang, H., Zheng, Z. Y., Yu, T., & Wang, J. L. (2021). Research on the invulnerability of regional network based on topology analysis. Journal of Communications, 42(11), 145-158. https://doi.org/10.11959/j.issn.1000−436x.2021179
Fu, Z. H., Sun, L., Lin, Z. Z., Wen, F. S., Zhu, B. Q., & Xu, L. Z. (2016). Bi-level network reconfiguration optimization based on node importance evaluation matrix. Electric Power Automation Equipment, 36(5): 37-4210. https://doi.org/16081/j.issn.1006-6047.2016.05.006
Hu, G., Xu, X., Gao, H., Guo, X. C. (2020). Node importance recognition algorithm based on adjacency information entropy in networks. Systems Engineering-Theory & Practice, 40(3), 714-725. https://doi.org/12011/1000-6788-2018-1805-12
Cui, X., Lu, Q. C., Xu, P. C., Wang, Z. X. & Qin, H. (2022). Critical station identification based on node importance contribution matrix in urban rail transit network. Journal of Railway Science and Engineering, 19(9), 2524-2531.
Ghorabaee, M. K., Amiri, M., Zavadskas, E.K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525
Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057
Pamučar, D., Vasin, L., & Lukovac, L. (2014, October). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. In XVI international scientific-expert conference on railway, railcon (pp. 89-92). https://doi.org/10.13140/2.1.2707.6807
Duckstein, L., & Opricovic, S. (1980) Multiobjective optimization in river basin development. Water Resources Research, 16(1), 14-20. https://doi.org/10.1029/WR016i001p0014.
Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & industrial engineering, 140, 106231. https://doi.org /10.1016/j.cie.2019.106231
Huang, C. L, & Yoon, K. (1981). Multiple attribute decision making: methods and applications. New York: Spriner-Verlag, 58-191. https://doi.org/10.1007/978-3-642-48318-9_3
Petrovic, I., & Kankaras, M. (2020). A hybridized IT2FS-DEMATEL-AHP-TOPSIS multicriteria decision making approach: Case study of selection and evaluation of criteria for determination of air traffic control radar position. Decision Making: Applications in Management and Engineering, 3(1), 146–164. https://doi.org/10.31181/dmame2003134p
Vasiljević, M., Fazlollahtabar, H., Stević, Ž., & Vesković, S. (2018). A rough multicriteria approach for evaluation of the supplier criteria in automotive industry. Decision Making: Applications in Management and Engineering, 1(1), 82–96. https://doi.org/10.31181/dmame180182v
Zhu, J. C., Liu, H., Wang, L. W., & Wu, T. (2021). Method for identifying key nodes based on overlap of network topology. Computer Application Research, 38(12), 3581-3585. https://doi.org/10.19734/j.issn.1001-3695.2021.05.0167
Hao, Z. G., & Qin, L. (2022). Method for discovering important nodes in food safety standard reference network based on multi-attribute comprehensive evaluation. Journal of Computer Applications, 42(04), 1178-1185. https://doi.org/10.11772/j.issn.1001-9081.2021071245
Qin, L., Yang, Z. L., & Huang, S. G. (2015). Synthesis evaluation method for node importance in complex networks. Computer Science, 42(2), 60-64.
Zuo, J. X., & Hua, X. (2022). Multi-attribute decision on the importance of UAV cluster network nodes. Journal of Xi'an University of Technology, 42(04), 422-426. https://doi.org/10.16185/j.jxatu.edu.cn.2022.04.402
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.