Research on the Resilient Evolutionary Game of Logistics Service Supply Chain with Government Participation

Zhang, Guangsheng, Wang, Xiao, Wang, Yanling and Kang, Jiayun (2022) Research on the Resilient Evolutionary Game of Logistics Service Supply Chain with Government Participation. MATHEMATICS, 10 (4). p. 630.

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Official URL: http://dx.doi.org/10.3390/math10040630

Abstract

<jats:p>The resilient strategy of logistics service supply chains with government participation is not only an effective measure for manufacturers and integrators to lower the risks but also an important component of the modern risk management capability of government. From the perspective of risk society, this paper presupposes the existence of emergencies, embeds the “resilient ability” into the logistics service supply chain, establishes a tripartite evolutionary game model among the government, manufacturers, and integrators, and systematically analyzes the strategy selection process under the participation of the government. On this basis, through numerical experiments, this paper examines the supervision sensitivity with different policies on a micro level, investigates the impact incurred by the change in governmental reward and punishment degree, and further complements the research on the complexity of the strategy selection process in reality. The results show that in the short run, regardless of the reward and punishment measure the government chooses, the manufacturers and integrators will choose not to adopt and implement the resilient supply chain strategy, while, in the long run, increasing manufacturers’ initial supervision and implementing relevant reward and punishment measures can guide integrators to choose the strategy. Increasing government integrators’ reward degree and reducing costs can thus promote the enthusiasm of integrators in resilient logistics service supply chains.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	logistics service supply chains, governmental reward and punishment, evolutionary game, resilient ability
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	19 Apr 2023 15:16
Last Modified:	16 Mar 2024 02:32
DOI:	10.3390/math10040630
Open Access URL:	https://doi.org/10.3390/math10040630
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169767