# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 1 Introduction  
The development of the Internet, automation, intelligent decision support, and other technologies has driven the manufacturing industry to transform towards digitalization, networking, and intelligence [1; 2; 3]. The manufacturing environment of enterprises has gradually expanded from the traditional workshop to large-scale, highly dynamic networked version [4; 5; 6; 7], such as the CMfg (Cloud Manufacturing), where the cloud platform has pooled massive virtualized manufacturing resources from geographically distributed providers [8; 9], including hardware resources (such as machining center, lathe, assembly line, materials, etc. ), software resources (such as CAD/CAE, simulation, computing capabilities), human resources, knowledge resources, and so on. Therefore, how to schedule these manufacturing resources to serve emerging manufacturing tasks in real-time is not only an urgent requirement in the CMfg environment but also a common problem of the generalized networked manufacturing models, such as Smart Manufacturing and Industrial 4.0 [10; 11]. Compared with the traditional workshop environment, real-time scheduling in the CMfg is more complicated because logistics factors need to be considered when using distributed massive heterogeneous manufacturing resources [12], and complex manufacturing projects bring precedence constraints at the same time. In addition, the provider needs to take into account its internal processing plan while coordinating with the demander, and the considerations of differences in management, specifications, and protocols among the enterprises are also needed [13; 14]. Although there have been many studies focused onmodeling, solving, and optimization of the scheduling problem in the workshop environment, their research results cannot be directly applied to the networked manufacturing environment because the scale of the problem is dramatically increased. In the academic consensus, the main kinds of users in the CMfg platform can be described by a so-called \"tri-group\" model [4; 15; 16], which is summarized as:  
**Demander**: Users who publish manufacturing tasks to the platform, and request to purchase the manufacturing resource services provided by the platform. **Provider**: Users who register manufacturing resources on the platform, providing virtualized manufacturing resources, such as software, equipment, materials, and labor. **Operator**: Users who operate and manage the platform; they (1) decompose the demands into manufacturing tasks, (2) encapsulate the registered manufacturing resources through virtualization technology, and (3) match the manufacturing services and provide decision support applications. The CMfg environment within the research scope of this article can be briefly described, as shown in Figure 1, and the meaning of the symbols is described in Table 1.  
Figure 1: Architecture of the Cloud Manufacturing platform. This architecture is mainly composed of three layers of functions, namely interface, application, and decision-making. The real-time scheduling problem comes from the decision-making layer. Accordingly, the process of the demander using the services provided by the platform can be described as follows:  
1. Demanders publish MP (Manufacturing Project) to the platform;
2. Platform decomposes MP into MTs (Manufacturing Tasks);
3. Platform discovers and matches MR (Manufacturing Resource) in type-matched MS (Manufacturing Service) for these MTs;
4. Platform allocates MR for the processing of MT.