LLM-Assisted Reinforcement Learning for U-Shaped and Circular Hybrid Disassembly Line Balancing in IoT-Enabled Smart Manufacturing

Abstract

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance.