云制造环境下3D打印服务组合优化研究

3D Printing Service Composition Optimization in Cloud Manufacturing

3D打印技术融合云制造模式形成的3D打印云服务具备低成本、跨区域、协同化的特点,可满足用户日益增长的个性化定制的需求。为解决3D打印云服务的合理分配问题和保障3D打印云平台高效运行,以时间和成本为优化目标,构建5约束优化模型,提出一种引入全局最优解信息的人工蜂群算法。通过仿真实验对算法性能、优化目标的选取进行分析,结果表明,改进的算法比对比算法具有更好的收敛性和稳定性,且与优化模型高度适配。

The 3D printing cloud service formed by the integration of 3D printing technology with the cloud manufacturing mode has the characteristics of low cost,cross-regional and synergy,so as to meet the growing needs of users for personalized customization.In order to solve the reasonable distribution of 3D printing cloud services and ensure the efficient operation of the 3D printing cloud platform,a 5-constraint optimization model is constructed with time and cost as the optimization goals,and an artificial bee colony algorithm that introduces global optimal solution information is proposed.The performance of the algorithm and the selection of optimization objectives are analyzed through simulation experiments.The results show that the improved algorithm has better convergence and stability than the comparison algorithm,and is highly suitable for the optimization model.