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<p>Machine deterioration is an inherent characteristic of many manufacturing systems and can significantly influence both processing performance and energy-related carbon emissions. However, most existing production scheduling studies assume stable machine conditions and neglect the dynamic emission variability induced by deterioration effects, particularly in deterioration-prone operations such as semiconductor wire bonding. To address this issue, this study develops a deterioration-aware carbon emission assessment model that quantitatively characterizes the relationship between machine deterioration and energy consumption. Based on this model, a sustainable production scheduling problem is formulated and solved using a hybrid improved particle swarm ant colony optimization (HIPSACO) algorithm. The proposed algorithm incorporates worst-solution guidance and adaptive memory mechanisms to enhance global exploration capability and to avoid premature convergence toward high-emission scheduling solutions. Extensive simulation-based computational experiments are conducted on small, medium, and large-scale instances to evaluate solution quality, convergence behavior, and robustness. The results demonstrate that the proposed approach effectively reduces carbon emissions while maintaining competitive production performance when compared with benchmark metaheuristic algorithms. These findings confirm the effectiveness and generalizability of integrating deterioration-aware emission modeling with advanced metaheuristic computation for complex scheduling problems.</p>

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Sustainable production scheduling during machine deterioration in wire bonding process of semiconductor assembly

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Yi-Chun Peng,
  • Jo-Chi Chao,
  • Kuan-Wei He,
  • Ping-Chien Chen

摘要

Machine deterioration is an inherent characteristic of many manufacturing systems and can significantly influence both processing performance and energy-related carbon emissions. However, most existing production scheduling studies assume stable machine conditions and neglect the dynamic emission variability induced by deterioration effects, particularly in deterioration-prone operations such as semiconductor wire bonding. To address this issue, this study develops a deterioration-aware carbon emission assessment model that quantitatively characterizes the relationship between machine deterioration and energy consumption. Based on this model, a sustainable production scheduling problem is formulated and solved using a hybrid improved particle swarm ant colony optimization (HIPSACO) algorithm. The proposed algorithm incorporates worst-solution guidance and adaptive memory mechanisms to enhance global exploration capability and to avoid premature convergence toward high-emission scheduling solutions. Extensive simulation-based computational experiments are conducted on small, medium, and large-scale instances to evaluate solution quality, convergence behavior, and robustness. The results demonstrate that the proposed approach effectively reduces carbon emissions while maintaining competitive production performance when compared with benchmark metaheuristic algorithms. These findings confirm the effectiveness and generalizability of integrating deterioration-aware emission modeling with advanced metaheuristic computation for complex scheduling problems.