Data-driven and autonomous manufacturing control in cyber-physical production systems

Modern manufacturing networks consist of cyber-physical systems (CPS) which offer an array of interesting capabilities, ranging from local computation over data generation to communication capabilities. As traditional control approaches fail to fully leverage these capabilities, the last decade has seen a renewed interest in distributed control approaches based on autonomous entities. In this article, we study the synergistic potentials of autonomous control and machine learning in a job-shop setting, addressing challenges of modern manufacturing such as market fluctuation and process time variance, thus leveraging the potentials of CPS in order to flexibly configure manufacturing networks and achieve cost-minimal production. We utilize a multi-agent based discrete-event simulation to compare this novel approach to a traditional heuristic, underlining the potentials and advantages of data-driven control approaches. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This article studies the synergistic potentials of autonomous control and machine learning in a job-shop setting, addressing challenges of modern manufacturing and leveraging the potentials of cyber-physical systems for cost-minimal production.