Tackling a Resource-Sharing Hybrid Disassembly Line Balancing Problem Using Reinforcement Learning

Driven by accelerated product obsolescence and frequent consumer replacements, electronic waste is growing rapidly. Waste recycling, as a core component of resource reuse, has become an important means of alleviating resource scarcity and reducing environmental pollution. In the process of recycling discarded products, the efficiency of disassembly operations is crucial. To improve disassembly efficiency and maximize resource utilization, this work proposes a hybrid disassembly line structure that incorporates both linear and U-shaped workstations. Shared labor is introduced between adjacent disassembly lines, allowing workers to flexibly execute tasks across lines. This resource-sharing mechanism enhances task coordination and reduces idle time, contributing to improved system efficiency. Using a precedence relationship graph to model dependencies among tasks, we develop a mathematical model aimed at maximizing profit. We use an exact solver to verify the model and adopt a variant of dueling deep Q-network, called PER-Dueling DQN (PDDQN), which incorporates prioritized experience replay to enhance sampling efficiency and solve the model optimally. A simulation environment aligned with this problem is constructed for the reinforcement learning agent. We compare the proposed method with other reinforcement learning approaches, including advantage actor-critic, proximal policy optimization, and trust region policy optimization. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of PDDQN are demonstrated, exhibiting significant advantages over other methods. Note to Practitioners - This study addresses efficiency challenges in electronic waste disassembly, where growing product turnover demands smarter resource management. A hybrid disassembly line structure combining linear and U-shaped layouts is proposed, with shared labor introduced between adjacent lines. Unlike conventional setups, this design allows workers to undertake tasks across multiple lines, improving coordination and reducing idle time. A reinforcement learning approach based on PER-Dueling DQN is used to optimize task allocation under these complex shared-resource conditions. Practitioners in remanufacturing and recycling industries can apply this method to enhance disassembly throughput, particularly in constrained environments where space and labor must be used efficiently.