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<p>The rapid obsolescence of electronic products highlights the critical need for efficient resource recovery through disassembly to support sustainable development. However, conventional disassembly methods often fail to handle complex disassembly sequences and dynamic operational constraints. To address the challenge of disassembly line balancing under worker fatigue, this study proposes an attention-based double deep Transformer Q-network (DDTQN) for minimizing disassembly time. It develops a disassembly time optimization model that incorporates fatigue-induced efficiency decay to enable the simulation of realistic operational conditions. By integrating an attention mechanism into the DDQ framework, the proposed approach enhances the capacity of the model to capture intricate task dependencies, thereby improving state representation, exploration efficiency, and long-term decision-making. Experimental results across three disassembly cases indicate that DDTQN reduces the average disassembly time by 19.37% compared with benchmark algorithms—including DDQN, deep Q-network (DQN), and advantage actor-critic. The successful application of DDTQN to marine equipment disassembly demonstrates its broad applicability and effectiveness, offering a robust solution for both general disassembly lines and specialized contexts such as ship recycling.</p>

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An improved double deep Q-network algorithm for disassembly line balancing problems considering worker fatigue

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Ruohong Shi,
  • Xiaowei Xu,
  • Zhongyuan Yang,
  • Shuo Shi

摘要

The rapid obsolescence of electronic products highlights the critical need for efficient resource recovery through disassembly to support sustainable development. However, conventional disassembly methods often fail to handle complex disassembly sequences and dynamic operational constraints. To address the challenge of disassembly line balancing under worker fatigue, this study proposes an attention-based double deep Transformer Q-network (DDTQN) for minimizing disassembly time. It develops a disassembly time optimization model that incorporates fatigue-induced efficiency decay to enable the simulation of realistic operational conditions. By integrating an attention mechanism into the DDQ framework, the proposed approach enhances the capacity of the model to capture intricate task dependencies, thereby improving state representation, exploration efficiency, and long-term decision-making. Experimental results across three disassembly cases indicate that DDTQN reduces the average disassembly time by 19.37% compared with benchmark algorithms—including DDQN, deep Q-network (DQN), and advantage actor-critic. The successful application of DDTQN to marine equipment disassembly demonstrates its broad applicability and effectiveness, offering a robust solution for both general disassembly lines and specialized contexts such as ship recycling.