Learning Evolution Modeling of Multi-cycle Nested Cloud Manufacturing Service Ecosystem

Authors

• Fang Li, School of Information, Xi'an University of Finance and Economics, Xi'an 710100, China; Key Laboratory of Intelligent Finance Collaboration and Trusted Computing, Shaanxi Provincial Institutions of Higher Education, Xi'an 710100, China
• Deyu Zhou, School of Software, Shandong University, Jinan 250101, China
• Gang Wang, School of Information, Xi'an University of Finance and Economics, Xi'an 710100, China; Key Laboratory of Intelligent Finance Collaboration and Trusted Computing, Shaanxi Provincial Institutions of Higher Education, Xi'an 710100, China
• Guangjun Liu, School of Information Engineering, Xi'an University, Xi'an 710065, China
• Qi Hu, School of Information Engineering, Xi'an University, Xi'an 710065, China

Abstract

Abstract: In view of the lack of comprehensive consideration of the individual adaptability changes of enterprises caused by the collaborative governance mechanism among multiple manufacturing units and the overall evolution trend of the system in existing learning evolution models, this proposed a learning evolution model of multi-cycle nested cloud manufacturing service ecosystem. At the micro level, the adaptive linkage decision-making among multiple manufacturing units within the enterprise was achieved in the individual layer through the nesting of planning-readiness-execution-assessment (PREA) loops and OODA loops; at the macro level, the closed-loop simulation of the individual layer, organizational layer, and social layer was achieved to provide an interpretable decision support tool for the development of digital and intelligent cloud manufacturing systems. The effectiveness of the model was verified by analyzing the survival quantity, aggregation degree, and overall capital of enterprises under different strategies and order allocation ratios in computational experiments.

Recommended Citation

Li, Fang; Zhou, Deyu; Wang, Gang; Liu, Guangjun; and Hu, Qi (2026) "Learning Evolution Modeling of Multi-cycle Nested Cloud Manufacturing Service Ecosystem," Journal of System Simulation: Vol. 38: Iss. 8, Article 10.
DOI: 10.16182/j.issn1004731x.joss.25-0993
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss8/10

First Page

2265

Last Page

2281

CLC

TP391.9

Recommended Citation

Li Fang, Zhou Deyu, Wang Gang, et al. Learning Evolution Modeling of Multi-cycle Nested Cloud Manufacturing Service Ecosystem[J]. Journal of System Simulation, 2026, 38(8): 2265-2281.

Corresponding Author

Zhou Deyu

DOI

10.16182/j.issn1004731x.joss.25-0993