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

1
Department of Industrial and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil
2
BIBA—Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, 28359 Bremen, Germany
3
Faculty of Production Engineering, University of Bremen, 28359 Bremen, Germany
*
Author to whom correspondence should be addressed.
Technologies 2018, 6(4), 107; https://doi.org/10.3390/technologies6040107
Received: 30 August 2018 / Revised: 31 October 2018 / Accepted: 5 November 2018 / Published: 16 November 2018
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Abstract

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. View Full-Text
Keywords: Multi-Agent Systems; Internet of Things; IoT; Digital Manufacturing; Job-shop Scheduling Problem Multi-Agent Systems; Internet of Things; IoT; Digital Manufacturing; Job-shop Scheduling Problem
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Leusin, M.E.; Frazzon, E.M.; Uriona Maldonado, M.; Kück, M.; Freitag, M. Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era. Technologies 2018, 6, 107.

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