Green and efficient-oriented human-robot hybrid partial destructive disassembly line balancing problem from non-disassemblability of components and noise pollution - Guo, Lei; Zhang, Zeqiang; Wu, Tengfei; Zhang, Yu; Zeng, Yanqing; Xie, Xinlan (Robotics and computer-integrated manufacturing, 2024)
•For the first time, destructive operation is introduced into the human-robot disassembly line balancing problem, with noise during disassembly is considered. A MIP model is also developed for this problem and the correctness of the model is verified using an exact solver and a meta-heuristic algorithm proposed.•To cope with large-scale cases and improve the computational efficiency, an improved grey wolf optimization (IGWO) algorithm is developed that incorporates a three-layer coding scheme and a Lévy flight mechanism. To test the performance of the proposed method, four classical disassembly cases are introduced and two multi-objective evaluation metrics are introduced to evaluate the algorithm.•Finally, a case study of engine was used to validate the practicality of the proposed methodology, which provided the company with a wide range of high-quality disassembly schemes. Additionally, the importance of the proposed method for practical engineering problems is demonstrated by comparing the human disassembly scheme, the robot disassembly scheme, and the scheme that ignores noise pollution and non-disassemblability of components. 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.