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A tripartite evolutionary game model for vehicle recall strategy optimization with dynamic penalty considerations

Year: 2025

Type: article

Abstract: To reduce the safety risks caused by vehicle defects, this study aims to optimize recall strategies and improve recall efficiency by analyzing the dynamic game behaviors of the government, manufacturers, and consumers in the vehicle recall process. This study proposes a tripartite evolutionary game model to formulate the strategic behaviors and interactions between the government, manufacturers, and consumers. Given three different penalty types (i.e., no-, static, and dynamic mechanisms), a number of simulations are conducted to analyze the impact of government regulatory intensity, manufacturer voluntary recall decisions, and consumer participation on recall effectiveness. Through the numerical simulations, it is found that the government regulatory intensity significantly influences manufacturer recall proactivity. The dynamic penalty mechanism encourages manufacturers to recall defective vehicles proactively and increases consumer willingness to participate, thereby improving the overall recall process performance. Additionally, strict government regulation combined with dynamic reward and penalty mechanisms can reduce safety risks and enhance consumer trust in the government and manufacturers.

Source: Journal of Industrial and Management Optimization

DOI: 10.3934/jimo.2025102

Authors: Guozhong Huang, Jianlan Zhou, Jing Shi, Yan Wang, Xuehong Gao

Institutions: University of Science and Technology Beijing, China University of Labor Relations, State Administration for Market Regulation

Language: English

Topic: Game Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: diamond

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China