%0 Journal Article %T 建筑垃圾资源化利用三方博弈与稳定策略优化研究
Research on the Evolutionary Game and Stability Strategy Optimization of Construction Waste Resource Utilization %A 周莹 %A 罗泽松 %A 于淼 %A 杨卓璠 %J Management Science and Engineering %P 294-307 %@ 2167-6658 %D 2025 %I Hans Publishing %R 10.12677/mse.2025.141030 %X 随着城市建筑垃圾总量不断扩大，为实现建筑垃圾资源化利用，需要厘清施工单位、社会资本及政府三者在其过程中的利益关系。首先通过三方演化博弈模型，分析不同策略组合下的均衡条件。进而引入动态奖惩制度，与静态制度对比分析系统的演化稳定策略。研究结果表明，施工单位、社会资本、政府分别稳定于“合法处理”“投资资源化项目”“实行激励约束政策”。当三方都选择积极处理建筑垃圾时，动态机制下的奖惩成本与三方策略选择无关，且动态机制下政府的激励约束政策效果更优。随后构建建筑垃圾资源化利用稳定策略优化模型，通过NSGA-II算法求得Pareto前沿面，得出内外部因素下的最佳组合方式，从而选择最为合适的方案。
With the continuous expansion of the total amount of urban construction waste, in order to realize the resource utilization of construction waste, it is necessary to clarify the relationship among the interests of the construction unit, social capital, and the government in its process. Firstly, the tripartite evolutionary game model is used to analyze the equilibrium conditions under different strategy combinations. Then, the dynamic reward and punishment system is introduced, and the evolutionary stability strategy of the system is compared with the static system. The results show that the construction unit, social capital, and government are stable in “legal treatment”, “investment in resource projects”, and “implementation of incentive and restraint policies”, respectively. When all three parties choose to actively deal with construction waste, the reward and punishment cost under the dynamic mechanism has nothing to do with the tripartite strategy choice, and the government’s incentive and restraint policies under the dynamic mechanism have a better effect. Then, the optimization model of the stable strategy of construction waste resource utilization was constructed, and the Pareto front was obtained through the NSGA-II algorithm, and the optimal combination of internal and external factors was obtained, so as to select the most suitable scheme. %K 演化博弈， %K 建筑垃圾， %K 仿真分析， %K NSGA-II算法
Evolutionary Game %K Construction Waste %K Simulation Analysis %K NSGA-II Algorithm %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=106467