Tripartite Evolutionary Game Analysis of the Industrial Internet Ecosystem

Abstract

The industrial internet holds immense potential for optimizing idle manufacturing services and driving a new wave of industrial transformation. To ensure its sustainable development requires a deep understanding of the collaborative dynamics among stakeholders and the factors shaping their strategic decisions. While previous research has examined stakeholder behavior within the industrial internet ecosystem, few studies have addressed the critical roles of government subsidies and perception biases—both of which are key in practice. This paper aims to bridge this gap and offer insights for the effective development of the industrial internet. To achieve this, we propose a tripartite evolutionary game model involving manufacturing service suppliers, requesters, and the industrial internet platform, grounded in Prospect Theory and Mental Accounting. We analyze the stability of equilibrium points and explore the effects of various factors, including the benefits of manufacturing service sharing, transaction fees, unmatching compensations, government subsidies, and reference points for benefits and costs, through simulation experiments. Our findings reveal that increasing the benefits of manufacturing service sharing and unmatching compensations, adjusting government subsidies, and reducing transaction fees can encourage greater participation from suppliers and requesters while motivating platforms to offer high-quality services. Furthermore, refining reference points by increasing the cost reference point and decreasing the valence reference point can help guide the system toward an optimal equilibrium. These insights provide practical recommendations for stakeholders, enabling them to design more effective strategies to foster the growth of the industrial internet ecosystem.

Keywords

Industrial internet ecosystem / tripartite evolutionary game / prospect theory and mental account / manufacturing service sharing

References

[1] AdamsonG, WangL, HolmM, MooreP. Cloud manufacturing-A critical review of recent development and future trends. International Journal of Computer Integrated Manufacturing, 2017, 30: 347-380

[2] BouzaryH, ChenF F. A classification-based approach for integrated service matching and composition in cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 2020, 66: 101989

[3] ChenX, PengY, WangX, WangP. Capacity sharing between competing manufacturers: A collective good or a detrimental effect?. International Journal of Production Economics, 2024, 268: 109107

[4] ChengY, XieY, ZhaoD, JiP, TaoF. Scalable hypernetwork-based manufacturing services supply demand matching toward industrial internet platforms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50: 5000-5014

[5] FuZ L, WangL, XueS J, MaJ, ZhangJ, GuoW. Tripartite evolutionary game analysis of the collective intelligence design ecosystem. Journal of Cleaner Production, 2022, 381: 135217

[6] HaoM, WangH, GaoK. The multiagent evolutionary game research of enterprise resource sharing on a shared manufacturing platform. Discrete Dynamics in Nature and Society, 2022, 2022: 2322887

[7] HaoR, ChengY, ZhangY, TaoF. Manufacturing service supply-demand optimization with dual diversities for industrial internet platforms. Computers & Industrial Engineering, 2021, 156: 107237

[8] HouX, LiJ, LiuZ, GuoY. Pareto and Kaldor–Hicks improvements with revenue-sharing and wholesale-price contracts under manufacturer rebate policy. European Journal of Operational Research, 2022, 298: 152-168

[9] Hu H, Li Y (2024). Carbon emission allowances purchasing decisions in supply chains under the cap-and-trade mechanism in China: An evolutionary game analysis. Kybernetes. https://doi.org/10.1108/K-02-2024-0457.

[10] JiangZ Z, FengG, GuoX, HeN, HuD. Financing a service-oriented manufacturer when facing the risk of bankruptcy. International Journal of Production Research, 2023, 62: 1-18

[11] KaaE J V D. Prospect theory and choice behaviour strategies: Review and synthesis of concepts from social and transport sciences. European Journal of Transport and Infrastructure Research, 2010, 10: 299-329

[12] KoussisN, SilaghiF. Revenue-sharing and volume flexibility in the supply chain. International Journal of Production Economics, 2023, 261: 108845

[13] LailiY, LinS, TangD. Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment. Robotics and Computer-Integrated Manufacturing, 2020, 61: 101850

[14] LiF, LiaoT W, CaiW, ZhangL. Multitask scheduling in consideration of fuzzy uncertainty of multiple criteria in service-oriented manufacturing. IEEE Transactions on Fuzzy Systems, 2020, 28: 2759-2771

[15] LiK, LiangC. Exploring the promotion of blockchain adoption in the healthcare industry through government subsidies. Kybernetes, 2023, 53: 5721-5739

[16] LiuB, ZhangZ. QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups. The International Journal of Advanced Manufacturing Technology, 2017, 88: 2757-2771

[17] LiuY, WangL, WangX V, XuX, ZhangL. Scheduling in cloud manufacturing: State-of-the-art and research challenges. International Journal of Production Research, 2019, 57: 4854-4879

[18] PergherI, de AlmeidaA T. A multi-attribute decision model for setting production planning parameters. Journal of Manufacturing Systems, 2017, 42: 224-232

[19] RenB, MeiS. Evolutionary game analysis of cooperation strategies for core enterprises of film copyright import and export under China’s supporting policies. Kybernetes, 2025, 6: 3562-3585

[20] RenM, ShaoL. A collaboration mechanism for service-oriented manufacturing processes with uncertain duration: A perspective of efficiency. International Journal of Computer Integrated Manufacturing, 2022, 35: 795-812

[21] RenM, ShaoL. Service Takt: A collaboration mechanism for service-oriented manufacturing. Computers & Industrial Engineering, 2023, 184: 109614

[22] RitzbergerK, WeibullJ W. Evolutionary selection in normal-form games. Econometrica, 1995, 63: 1371-1399

[23] SenbilM, KitamuraR. Reference points in commuter departure time choice: A prospect theoretic test of alternative decision frames. Journal of Intelligent Transportation Systems, 2004, 8: 19-31

[24] TaoF, ChengY, XuL D, ZhangL, LiB H. CCIOTCMfg: Cloud computing and Internet of things-based cloud manufacturing service system. IEEE Transactions on Industrial Informatics, 2014, 10: 1435-1442

[25] TaoF, QiQ. New IT driven service-oriented smart manufacturing: Framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49: 81-91

[26] TongH, ZhuJ. An RNN-MCDA method to support efficient and stable matching under uncertain preferences in large-scale sharing platforms. Applied Soft Computing, 2022, 124: 108927

[27] TverskyA, KahnemanD. Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 1992, 5: 297-323

[28] WangT, LiH, ZhouX, HuangB, ZhuH. A prospect theory-based three-way decision model. Knowledge-Based Systems, 2020, 203: 106129

[29] WangT, ZhangH. Evolutionary game study on manufacturing capacity sharing based on block chain. Proceedings of the 3rd International Conference on Electronics, Communications and Information Technology (CECIT), 2022373-378

[30] WangT Y, ZhangH. Blockchain-based tripartite evolutionary game study of manufacturing capacity sharing. Advances in Production Engineering & Management, 2023, 18: 225-236

[31] XiaH, ZhangR, ChengX, QiuT, WuD O. Two-stage game design of payoff decision-making scheme for crowdsourcing dilemmas. IEEE/ACM Transactions on Networking, 2020, 28: 2741-2754

[32] XiaX, ZengX, WangW, LiP. Impact of carbon quota allocation on equipment recycling by service-oriented manufacturers: The design of a coordination mechanism. Journal of Cleaner Production, 2023, 431: 139736

[33] YangB, WangS, ChengQ, JinT. Scheduling of field service resources in cloud manufacturing based on multi-population competitive-cooperative GWO. Computers & Industrial Engineering, 2021, 154: 107104

[34] YangX, LiuY, WangL, WeiJ. Evolutionary game analysis of manufacturing resource sharing in automotive supply chain based on cloud platform. Proceedings of the 7th International Conference on Computer and Communication Systems (ICCCS), 2022880-885

[35] ZhangG, WangX, WangY, XuJ. A tripartite evolutionary game for the regional green logistics: The roles of government subsidy and platform’s cost-sharing. Kybernetes, 2024, 53: 216-237

[36] ZhangS, LuoL, LiY, ShanR. The analysis of manufacturing service ecosystem’s evolution stability based on evolutionary game. Journal of Physics: Conference Series, 2021, 1827: 012092

[37] ZhangW, ZhengY, MaW, AhmadR. Multi-task scheduling in cloud remanufacturing system integrating reuse, reprocessing, and replacement under quality uncertainty. Journal of Manufacturing Systems, 2023, 68: 176-195

[38] ZhangX, LiJ, DanB. Match of the cross-period capacity on the third-party shared manufacturing platform with demand delay. Expert Systems with Applications, 2024, 240: 122479

[39] ZhaoM, LiB, RenJ, HaoZ. Competition equilibrium of ride-sourcing platforms and optimal government subsidies considering customers’ green preference under peak carbon dioxide emissions. International Journal of Production Economics, 2023, 255: 108679

[40] ZouX, ChenJ, GaoS. Network effect in shared supply chain platform value co-creation behavior in evolutionary game. Journal of Intelligent & Fuzzy Systems, 2021, 41: 4713-4724

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Systems Engineering Society of China and Springer-Verlag GmbH Germany