关键词: 云制造, 在线调度, 需求预测, 深度学习, 服务租赁配置

Abstract: Dynamic optimization of cloud service renting configuration is the key to achieving efficient,high-quality and on-demand service formassive stochastic manufacturing tasks in cloud manufacturing.Manufacturing tasks and manufacturing services were analyzed and defined in the cloud manufacturing environment,and the optimization objectives were set in three aspects: service renting cost,service utility and task delay.Under the condition of capacity constraints,the re-scheduling mode triggered by timing length or fixed number of tasks was established.LSTM-based demand forecasting approach was adopted to realize the forward-looking dynamic scheduling of cloud manufacturing services and the optimization for renting configuration.The experimental results showed that the demand forecasting based online-scheduling could reduce the service renting cost by 4.50%,improve the service utility by 0.86% and reduce the value of average task delay by 5.15% in comparison with the method without forecasting.

Key words: cloud manufacturing, online scheduling, demand prediction, deep learning, service renting configuration