# Smart Manufacturing Scheduling Approaches--Systematic Review and Future Directions
## 5 Scheduling in the Context of Smart Manufacturing and Next Steps
### Opportunities Future Work  
Nowadays, manufacturing systems are becoming enormous living robotic environments, and, in some cases, there is limited human presence. There are smart products, which constantly give feedback about their own status to the system, and smart resources, which can do the same and inform the system in real-time if some problem occurs in the production line. This intelligence within the system allows the different components to communicate and interact in order to reach common objectives. Nevertheless, on the other side, it also allows the extraction of more data and information from the system than ever before and transforms that information into useful knowledge. Knowledge can be used to improve multiple strands of the system, not only within the factory but also across the entire supply chain. This fact can immensely improve production scheduling systems, which are able to extract dynamically and in real-time more and more data from a vast variety of smart components, which can help to provide more robust and efficient scheduling solutions. However, in recent years there has been more and more interoperability between tools on shopfloors and existing legacy solutions are not able to account for this. Although there is a huge complexity involved in developing smart manufacturing systems and there may be some restrictions regarding the process and memory power, nowadays the main limitations are not related to the hardware nor to the connection between entities since the emergence of cyber-physical systems has allowed all kind of entities on the shopfloor to be virtualized and connected together, thus permitting the easy execution of monitoring and controlling activities in the production line. One of the main problems is related to the linking between different types of data sources and how they need to cooperate to achieve better and more efficient performances. Consequently, it is necessary to go one step further and direct more efforts towards modeling, optimization, and standardization of manufacturing systems [105]. Therefore, to harmonize the designing and development of manufacturing systems, some reference architectures have emerged over recent years, such as ISA-95 [106], 5C architecture [107], Smart Grid Architecture Model--SGAM [108], Industrial Internet Reference Architecture--IIRA [109], or Reference Architectural Model for Industrie 4.0--RAMI4.0 [110]. These architectures aim to ensure a common comprehension, achieve standardization, enable semantic interoperability, and provide consistent operation models for the system. By adopting a reference architecture in manufacturing scheduling it will be possible to manage all the information coming from different sources in a consistent and homogeneous way and apply a core scheduling solution to different scenarios. One of the possible ways to achieve this is to create an Asset Administration Shell (AAS) for scheduling and consequently for all other entities related to scheduling, under the RAMI4.0 architecture. An AAS consists of transforming physical components such as robots, machines, or devices and, similarly, intangible assets such as functions, plans, or an entire network into Industrie 4.0 components, which will then allow standardization as much as possible for solutions in engineering, operation, and management, and implementation of a heterogeneous communication structure in a smart manufacturing-oriented system. The AAS is the virtual representation of the asset that encompasses all the information and technical functionalities of the asset and manages communications with other Industrie 4.0 Components [110]. Through describing and modeling the asset components in smart manufacturing environments aligned with RAMI4.0 architecture, it will be possible to standardize and optimize the scheduling development process.