LAUSR

In service-oriented manufacturing modes, service scheduling is important for providing just-in-time delivery of manufacturing services to customers. In this paper, the mathematical model of the dynamic cloud manufacturing scheduling problem is constructed, and a scheduling method based on dynamic data-driven simulation is proposed to improve the scheduling performance. The framework, scheduling rules and simulation strategies are discussed in detail. Specifically, three single scheduling rules and three combined scheduling rules are designed based on service time, logistics time, and subtask queue status of candidate services. The real-time information of tasks and services is involved in the simulation strategy to select better scheduling rules. The simulation models of proposed scheduling strategies are constructed and simulated in the Simio software, which is connected to a real-time information database. The proposed method is tested through a case study of numerical control machining in cloud manufacturing, and the results show that the proposed method is promising.