Game analysis on regenerative synergy mechanism of the supply chain of integrate infrastructure engineering

Na Zhao; Bingqi Ma; Xiaolian Li; Na Zhao; Bingqi Ma; Xiaolian Li

doi:10.3934/mbe.2023440

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10027-10042. doi: 10.3934/mbe.2023440

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Game analysis on regenerative synergy mechanism of the supply chain of integrate infrastructure engineering

School of Management, Harbin University of Commerce, Harbin 150000, China

Academic Editor: Biswajit Sarkar

Received: 31 December 2022 Revised: 16 March 2023 Accepted: 19 March 2023 Published: 28 March 2023

How to ensure the smooth implementation of convergent infrastructure engineering as the risk of sudden public events persists, allowing the engineering supply chain companies to break through the blockages to regenerate collaboratively and form a regenerated collaborative union. By establishing a mathematical game model, this paper explores the synergistic mechanism of supply chain regeneration for convergent infrastructure engineering, which takes into account cooperation and competition, investigates the impact of supply chain nodes' regeneration capacity and economic performance, as well as the dynamic changes in the importance weights of supply chain nodes, when adopting the collaborative decision of supply chain regeneration, the benefits of the supply chain system, are more than those when suppliers and manufacturers "act of one's own free will" by making decentralized decisions to undertake supply chain regeneration separately. All the investment costs of supply chain regeneration are higher than those in non-cooperative games. Based on the comparison of equilibrium solutions, it was found that exploring the collaborative mechanism of its convergence infrastructure engineering supply chain regeneration provides useful arguments for the emergency re-engineering of the engineering supply chain with a tube mathematical basis. Through constructing a dynamic game model for the exploration of the supply chain regeneration synergy mechanism, this paper provides methods and support for the emergency synergy among subjects of infrastructure construction projects, especially in improving the mobilization effectiveness of the entire infrastructure construction supply chain in critical emergencies and enhancing the emergency re-engineering capability of the supply chain.
- integrate infrastructure engineering,
- supply chain,
- regenerative mechanism,
- synergy mechanism,
- game analysis
Citation: Na Zhao, Bingqi Ma, Xiaolian Li. Game analysis on regenerative synergy mechanism of the supply chain of integrate infrastructure engineering[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10027-10042. doi: 10.3934/mbe.2023440

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Abstract

How to ensure the smooth implementation of convergent infrastructure engineering as the risk of sudden public events persists, allowing the engineering supply chain companies to break through the blockages to regenerate collaboratively and form a regenerated collaborative union. By establishing a mathematical game model, this paper explores the synergistic mechanism of supply chain regeneration for convergent infrastructure engineering, which takes into account cooperation and competition, investigates the impact of supply chain nodes' regeneration capacity and economic performance, as well as the dynamic changes in the importance weights of supply chain nodes, when adopting the collaborative decision of supply chain regeneration, the benefits of the supply chain system, are more than those when suppliers and manufacturers "act of one's own free will" by making decentralized decisions to undertake supply chain regeneration separately. All the investment costs of supply chain regeneration are higher than those in non-cooperative games. Based on the comparison of equilibrium solutions, it was found that exploring the collaborative mechanism of its convergence infrastructure engineering supply chain regeneration provides useful arguments for the emergency re-engineering of the engineering supply chain with a tube mathematical basis. Through constructing a dynamic game model for the exploration of the supply chain regeneration synergy mechanism, this paper provides methods and support for the emergency synergy among subjects of infrastructure construction projects, especially in improving the mobilization effectiveness of the entire infrastructure construction supply chain in critical emergencies and enhancing the emergency re-engineering capability of the supply chain.

References

[1]	M. A. Momeni, S. Yaghoubi, M. R. M. Aliha, A cost sharing-based coordination mechanism for multiple deteriorating items in a one manufacture-one retailer supply chain, J. Ind. Eng. Manage. Stud., 6 (2019), 79–110. https://doi.org/10.22116/jiems.2019.87661 doi: 10.22116/jiems.2019.87661
[2]	D. Berlin, A. Feldmann, C. Nuur. The relatedness of open-and closed-loop supply chains in the context of the circular economy, framing a continuum, Cleaner Logistics Supply Chain, 2022 (2022), 100048. https://doi.org/10.1016/j.clscn.2022.100048 doi: 10.1016/j.clscn.2022.100048
[3]	D. Das, A. Datta, P. Kumar, Y. Kazancoglu, M. Ram, Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach, Oper. Manage. Res., 15 (2022), 249–267. https://doi.org/10.1007/s12063-021-00200-4 doi: 10.1007/s12063-021-00200-4
[4]	M. S. Golan, L. H. Jernegan, I. Linkov, Trends and applications of resilience analytics in supply chain modeling: Systematic literature review in the context of the COVID-19 pandemic, Environ. Syst. Decisions, 40 (2020), 222–243. https://doi.org/10.1111/jscm.12162 doi: 10.1007/s10669-020-09777-w
[5]	M. Bosch-Rekveldt, Y. Jongkind, H. Mooi, H. Bakker, A. Verbraeck, Grasping project complexity in large engineering projects: The TOE (technical, organizational and environmental) framework, Int. J. Proj. Manage., 29 (2011), 728–739. https://doi.org/10.1016/j.ijproman.2010.07.008 doi: 10.1016/j.ijproman.2010.07.008
[6]	M. Raweewan, W. G. Ferrell Jr, Information sharing in supply chain collaboration, Comput. Ind. Eng., 126 (2018), 269–281. https://doi.org/10.1016/j.cie.2018.09.042 doi: 10.1016/j.cie.2018.09.042
[7]	L. Chen, X. Zhao, O. Tang, L. Price, S. Zhang, W. Zhu, Supply chain collaboration for sustainability: A literature review and future research agenda, Int. J. Prod. Econ., 194 (2017), 73–87. https://doi.org/10.1016/j.ijpe.2017.04.005 doi: 10.1016/j.ijpe.2017.04.005
[8]	S. V. S. Padiyar, V. Vandana, N. Bhagat, S. R. Singh, B. Sarkar, Joint replenishment strategy for deteriorating multi-item through multi-echelon supply chain model with imperfect production under imprecise and inflationary environment, RAIRO Oper. Res., 56 (2022), 3071–3096. https://doi.org/10.1051/ro/2022071 doi: 10.1051/ro/2022071
[9]	R. Müller, R. Turner, E. S. Andersen, J. Shao, Ø. Kvalnes, Ethics, trust, and governance in temporary organizations, Proj. Manage. J., 45 (2014), 39–54. https://doi.org/10.1002/pmj.21432 doi: 10.1002/pmj.21432
[10]	J. Clausen, J. Larsen, A. Larsen, J. Hansen, Disruption management-operations research between planning and execution, Or-ms Today, 28 (2001), 40–43.
[11]	A. P. Barroso, V. H. Machado, A. R. Barros, V. C. Machado, Toward a resilient supply chain with supply disturbances in 2010 IEEE International Conference on Industrial Engineering and Engineering Management, 46 (2010), 245–249. https://doi.org/10.1109/IEEM.2010.5674462
[12]	T. M. Simatupang, A. C. Wright, R. Sridharan, The knowledge of coordination for supply chain integration, Bus. Process Manage. J., 8 (2002), 289–308. https://doi.org/10.1108/14637150210428989 doi: 10.1108/14637150210428989
[13]	K. B. Hendricks, V. R. Singhal, Association between supply chain glitches and operating performance, Manage. Sci., 51 (2005), 695–711. https://doi.org/10.1287/mnsc.1040.0353 doi: 10.1287/mnsc.1040.0353
[14]	N. O. Hohenstein, Supply chain risk management in the COVID-19 pandemic: Strategies and empirical lessons for improving global logistics service providers' performance, Int. J. Logistics Manage., 33 (2022), 1336–1365. https://doi.org/10.1108/IJLM-02-2021-0109 doi: 10.1108/IJLM-02-2021-0109
[15]	M. Hussain, M. Malik, Organizational enablers for circular economy in the context of sustainable supply chain management, J. Cleaner Prod., 256 (2020), 120375. https://doi.org/10.1016/j.jclepro.2020.120375 doi: 10.1016/j.jclepro.2020.120375
[16]	D. Ivanov, Exiting the COVID-19 pandemic: After-shock risks and avoidance of disruption tails in supply chains, Ann. Ope. Res., 2021 (2021), 1–18. https://doi.org/10.1007/s10479-021-04047-7 doi: 10.1007/s10479-021-04047-7
[17]	V. Jain, S. Kumar, A. Kumar, C. Chandra. An integrated buyer initiated decision-making process for green supplier selection, J. Manuf. Syst., 41 (2016), 256–265. https://doi.org/10.1016/j.jmsy.2016.09.004 doi: 10.1016/j.jmsy.2016.09.004
[18]	M. A. Momeni, V. Jain, K. Govindan, A. Mostofi, S. J. Fazel, A novel buy-back contract coordination mechanism for a manufacturer-retailer circular supply chain regenerating expired products, J. Cleaner Prod., 375 (2022), 133319. https://doi.org/10.1016/j.jclepro.2022.133319 doi: 10.1016/j.jclepro.2022.133319
[19]	F. Naz, A. Kumar, A. Majumdar, R. Agrawal, Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research, Oper. Manage. Res., 15 (2022), 378–398. doi: 10.1007/s12063-021-00208-w
[20]	P. K. Tarei, G. Kumar, M. Ramkumar, A mean-variance robust model to minimize operational risk and supply chain cost under aleatory uncertainty: A real-life case application in petroleum supply chain, Comput. Ind. Eng., 166 (2022), 107949. https://doi.org/10.1016/j.cie.2022.107949 doi: 10.1016/j.cie.2022.107949
[21]	A. Rehman, M. S. S. Jajja, S. Farooq, Manufacturing planning and control driven supply chain risk management: A dynamic capability perspective, Trans. Res. Part E, 167 (2022), 102933. https://doi.org/10.1016/j.tre.2022.102933 doi: 10.1016/j.tre.2022.102933
[22]	H. Zhao, C. Zhang, An online-learning-based evolutionary many-objective algorithm, Inf. Sci., 509 (2020), 1–21. https://doi.org/10.1016/j.ins.2019.08.069 doi: 10.1016/j.ins.2019.08.069
[23]	M. A. Dulebenets, An adaptive polyploid memetic algorithm for scheduling trucks at a cross-docking terminal, Inf. Sci., 565 (2021), 390–421. https://doi.org/10.1016/j.ins.2021.02.039 doi: 10.1016/j.ins.2021.02.039
[24]	M. Kavoosi, M. A. Dulebenets, O. Abioye, J. Pasha, O. Theophilus, H. Wang, et al., Berth scheduling at marine container terminals: A universal island-based metaheuristic approach, Maritime Bus. Rev., 5 (2019), 30–66. https://doi.org/10.1108/MABR-08-2019-0032 doi: 10.1108/MABR-08-2019-0032
[25]	E. B. Tirkolaee, A. Goli, S. Gütmen, G. W. Weber, K. Szwedzka, A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms, Ann. Oper. Res., 2022 (2022), 1–26. https://doi.org/10.1007/s10479-021-04486-2 doi: 10.1007/s10479-021-04486-2
[26]	A. Özmen, E. Kropat, G. W. Weber, Spline regression models for complex multi-modal regulatory networks, Optim. Methods Software, 29 (2014), 515–534. https://doi.org/10.1080/10556788.2013.821611 doi: 10.1080/10556788.2013.821611
[27]	A. Özmen, E. Kropat, G. W. Weber, Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty, Optimization, 66 (2017), 2135–2155. https://doi.org/10.1080/02331934.2016.1209672 doi: 10.1080/02331934.2016.1209672

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