Industrial Internet

Scheduling (production processes)

Job shop

Mathematics

Operations research

Job shop scheduling

Operating system

Distributed computing

Data mining

Statistics

World Wide Web

Engineering

Flow shop scheduling

Industrial engineering

Embedded system

Artificial intelligence

Data exchange

Cyber-physical system

Operations management

Industry 4.0

Computer science

Flexibility (engineering)

Routing (electronic design automation)

Embedding

Scalability

Database

Internet of Things

Technologies

Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era