Current research on the disassembly line balancing problem ignores the influence of non-disassemblability of components. And this problem can lead to failure of the disassembly task, which can seriously affect the disassembly efficiency. This study integrates destructive operation into the human-robot disassembly line while considering noise. First, a mixed integer programming model is established for human-robot hybrid partial destructive disassembly line balancing problem to accurately obtain the number of stations, smoothness index, costs and negative impact of noise pollution on workers. Then, an improved grey wolf optimization algorithm is proposed for the NP-hard characteristic of problem. A three-layer encoding and two-stage decoding strategy is designed to constrain the uniqueness of the solution, considering the noise constraints, and the different disassembly times of the human-robot. A disturbance factor is also designed to prevent local optimality, which enhances the performance of the proposed algorithm. Different cases are also used to verify the correctness and superiority of the proposed method. Finally, an engine case is used to validate the practicality of the proposed method. The results of the comparison of the different disassembly schemes show that: (1) The proposed algorithm outperforms the three classical Swarm Intelligence methods and other eleven algorithms in the disassembly line balancing problem. (2) The human-robot hybrid partial destructive disassembly line can effectively avoid the problem of task failure, and the smoothing index is reduced by 12.27 % compared with the original scheme. Disassembly costs increased by 1.28 %, but this was minimal compared to line-wide smooth running and worker health. (3) The human-robot hybrid disassembly line is more appropriate to solve the actual production process compared to worker disassembly and robot disassembly, and has a greater advantage in solving the actual disassembly line balance problem.

中文翻译：

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面向绿色高效的人机混合部分破坏性拆卸线平衡问题（部件不可拆卸性和噪声污染）


目前对拆卸线平衡问题的研究忽略了部件不可拆卸性的影响。而这个问题会导致反汇编任务失败，严重影响反汇编效率。这项研究将破坏性操作集成到人机拆卸线中，同时考虑了噪声。首先，针对人机混合部分破坏性拆卸线平衡问题建立混合整数规划模型，准确获取工位数量、平稳指数、成本以及噪声污染对工人的负面影响。然后针对问题的NP难特性，提出了一种改进的灰狼优化算法。考虑到噪声约束以及人机机器人的不同拆卸时间，设计了三层编码和两级解码策略来约束解决方案的唯一性。还设计了扰动因子来防止局部最优，从而增强了所提出算法的性能。并通过不同的案例验证了所提方法的正确性和优越性。最后通过发动机实例验证了所提方法的实用性。不同拆卸方案的比较结果表明：（1）所提出的算法在拆卸线平衡问题上优于三种经典群体智能方法和其他十一种算法。 （2）人机混合部分破坏性拆装线可以有效避免任务失败问题，平滑指数较原方案降低12.27%。拆卸成本增加了 1.28%，但与全生产线的平稳运行和工人健康相比，这是微乎其微的。 (3)人机混合拆装线相比工人拆装和机器人拆装更适合解决实际生产过程，在解决实际拆装线平衡问题上具有较大优势。