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

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.