Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8–10, 2023, Guangzhou, China

Research Article

Scheduling Modeling and Optimization of 3D Print Task in Energy-consumption-aware Cloud Manufacturing Environment Based on Improved Dung Beetle Algorithm

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  • @INPROCEEDINGS{10.4108/eai.8-12-2023.2344356,
        author={Xiang  Lai},
        title={Scheduling Modeling and Optimization of 3D Print Task in Energy-consumption-aware Cloud Manufacturing Environment Based on Improved Dung Beetle Algorithm},
        proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China},
        publisher={EAI},
        proceedings_a={MSIEID},
        year={2024},
        month={4},
        keywords={cloud manufacturing scheduling; 3d printing; energy-consumption-aware; carbon emissions; dung beetle optimization algorithm},
        doi={10.4108/eai.8-12-2023.2344356}
    }
    
  • Xiang Lai
    Year: 2024
    Scheduling Modeling and Optimization of 3D Print Task in Energy-consumption-aware Cloud Manufacturing Environment Based on Improved Dung Beetle Algorithm
    MSIEID
    EAI
    DOI: 10.4108/eai.8-12-2023.2344356
Xiang Lai1,*
  • 1: School of Maritime Economics and Management of Dalian Maritime University
*Contact email: 1025730977@qq.com

Abstract

The challenge of selecting and scheduling cloud manufacturing services has been widely concerned in optimizing resource allocation and meeting user needs. However, most existing methods do not take into account the preheating of manufacturing equipment and the energy consumption of the processing process, resulting in wasted energy and thus increased carbon emissions. To mitigate carbon emissions from manufacturing while ensuring Service Quality (QoS), this study develops a model for scheduling 3D printing tasks in cloud manufacturing. This model aims for minimal completion times, reduced 3D printer service loads, and decreased carbon emissions. An enhanced dung beetle optimization algorithm, drawing on an advanced sinusoidal method, is introduced to equip dung beetles with comprehensive global exploration and localized development capabilities. This expansion of their search domain enhances global exploration, diminishes the risk of local optima, and incorporates mutation operators for variability. Empirical results demonstrate the algorithm's efficacy in addressing real-world applications.