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In recent years, many meta-heuristics have been proposed to solve the Cloud Manufacturing-based 3D Printing Service Selection and Scheduling problem (CM3DSS). However, the performance of meta-heuristics is dependent on the problem. To obtain high-quality solutions across different CM3DSS, this paper proposes a new adaptive hyper-heuristic selection strategy (ASHH). Besides, a new realistic problem model, 3D printing service selection in cloud manufacturing (3DSC) is built. The 3DSC ensures the minimization of user costs, production time, and energy consumption. Experimental results confirm that ASHH has a significant competitive advantage in solving the 3DSC.

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ASHH: A Hyper-heuristic for Service Selection and Scheduling of 3D Printing Cloud Manufacturing Platform

  • 会议论文

  • 发表日期：

    2025年

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  • Sihao Li,
  • Cuixia Li,
  • Jun Qiao,
  • Shuyan Zhang

摘要

In recent years, many meta-heuristics have been proposed to solve the Cloud Manufacturing-based 3D Printing Service Selection and Scheduling problem (CM3DSS). However, the performance of meta-heuristics is dependent on the problem. To obtain high-quality solutions across different CM3DSS, this paper proposes a new adaptive hyper-heuristic selection strategy (ASHH). Besides, a new realistic problem model, 3D printing service selection in cloud manufacturing (3DSC) is built. The 3DSC ensures the minimization of user costs, production time, and energy consumption. Experimental results confirm that ASHH has a significant competitive advantage in solving the 3DSC.