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<p>Additive manufacturing (AM) in the context of large-scale personalization, an emerging production pattern, aims to improve profitability for modern industrial enterprises. However, incorporating numerous heterogeneous parts and packing constraints complicates the production scheduling problem, intensifying the demand for efficient scheduling. Moreover, the support structure volume affects the efficiency and cost of the AM process. A novel method for the multi-objective AM scheduling problem (MAMSP), which considers support structure volume, is proposed to address the challenges above. First, a mathematical model for the MAMSP is formulated, aiming to minimize both makespan and support structure volume. Then, a problem-specific improved NSGA-II is proposed to solve the model. Finally, different scale instances are tested to compare the performance, and the simulation shows that the proposed algorithm can effectively solve the MAMSP. Furthermore, its modular and computationally efficient design facilitates potential deployment on high-performance computing platforms, enabling real-time scheduling for large-scale personalized AM production.</p>

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Multi-objective scheduling for unrelated parallel machines toward large-scale personalized customization in additive manufacturing

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Qingze Tan,
  • Xingyu Jiang,
  • Guangdong Tian,
  • Zhiqiang Tian,
  • Jiazhen Li,
  • Yichen Wang,
  • Baohai Zhao,
  • Guozhe Yang

摘要

Additive manufacturing (AM) in the context of large-scale personalization, an emerging production pattern, aims to improve profitability for modern industrial enterprises. However, incorporating numerous heterogeneous parts and packing constraints complicates the production scheduling problem, intensifying the demand for efficient scheduling. Moreover, the support structure volume affects the efficiency and cost of the AM process. A novel method for the multi-objective AM scheduling problem (MAMSP), which considers support structure volume, is proposed to address the challenges above. First, a mathematical model for the MAMSP is formulated, aiming to minimize both makespan and support structure volume. Then, a problem-specific improved NSGA-II is proposed to solve the model. Finally, different scale instances are tested to compare the performance, and the simulation shows that the proposed algorithm can effectively solve the MAMSP. Furthermore, its modular and computationally efficient design facilitates potential deployment on high-performance computing platforms, enabling real-time scheduling for large-scale personalized AM production.