With the continuous growth of the number of end-of-life vehicles and the rapid increase in the ownership of pure electric vehicles, the automobile disassembly industry is facing the challenge of transitioning from the traditional fuel vehicles to the mixed disassembly of fuel vehicles and pure electric vehicles. In order to cope with the uncertainty of recycling quantity and the demand of mixed-model disassembly of multiple vehicle types, this paper designs a multi-parallel mixed-model disassembly line (MPMDL), and constructs a corresponding mixed-integer planning model for the equilibrium optimization problem of this disassembly line with the optimization objectives of the minimum number of workstations, the minimum fatigue level of workers and the minimum energy consumption. Combining the differences in disassembly processes between fuel vehicles and pure electric vehicles, an improved non-dominated sorting multi-objective genetic algorithm (INSGA-III) based on the distribution of feasible solutions and dynamic search resource allocation is designed to solve this multi-objective dynamic balance optimization problem, and the two-stage dynamic adjustment strategy is adopted to realize the adaptive adjustment of the disassembly line under the uncertainty of the recycling quantity, and, recently, arithmetic validation is carried out. The results show that the proposed method can effectively improve the resource utilization efficiency, reduce energy consumption, alleviate the workers' load, and provide multiple high-quality disassembly solutions under the multi-objective trade-off. Compared with mainstream multi-objective optimization algorithms, the INSGA-III algorithm shows significant advantages in terms of solution quality, convergence and stability. This study provides a green, efficient and flexible solution for hybrid disassembly of fuel and pure electric vehicles.

翻译：随着报废车辆数量的持续增长和纯电动车保有量的快速增加，汽车拆卸行业正面临从传统燃油车向燃油车与纯电动车混合拆卸转型的挑战。为应对回收数量的不确定性及多车型混合模型拆卸的需求，本文设计了一种多并行混合模型拆卸线（MPMDL），并以最小工作站数量、最低工人疲劳度和最低能耗为优化目标，构建了针对该拆卸线平衡优化问题的混合整数规划模型。结合燃油车与纯电动车在拆卸工艺上的差异，设计了一种基于可行解分布与动态搜索资源分配的改进非支配排序多目标遗传算法（INSGA-III）来解决这一多目标动态平衡优化问题，并采用两阶段动态调整策略实现回收数量不确定条件下拆卸线的自适应调整，最后进行了算例验证。结果表明，所提方法能有效提高资源利用效率、降低能耗、缓解工人负荷，并在多目标权衡下提供多种高质量拆卸方案。与主流多目标优化算法相比，INSGA-III算法在解的质量、收敛性和稳定性方面均表现出显著优势。本研究为燃油车与纯电动车的混合拆卸提供了绿色、高效且灵活的解决方案。