Cloud Based Decision Making for Multi-agent Production Systems

Abstract

The use of multi-agent systems (MAS) as a distributed control method for shop-floor manufacturing control applications has been extensively researched. MAS provides new implementation solutions for smart manufacturing requirements such as the high dynamism and flexibility required in modern manufacturing applications. MAS in smart manufacturing is becoming increasingly important to achieve increased automation of machines and other components. Emerging technologies like artificial intelligence, cloud-based infrastructures, and cloud computing can also provide systems with intelligent, autonomous, and more scalable solutions. In the current work, a decision-making framework is proposed based on the combination of MAS cloud computing, agent technology, and machine learning. The framework is demonstrated in a quality control use case with vision inspection and agent-based control. The experiment utilizes a cloud-based machine learning pipeline for part classification and agent technology for routing. The results show the applicability of the framework in real-world scenarios bridging cloud service-oriented architecture with agent technology for production systems.

Big Data Life Cycle in Shop-Floor–Trends and Challenges

“…Smart sensors have also been used to monitor best conditions of machines, e.g., in terms of temperature [42]. In addition, information of images (quality control) has been used as a decision factor for autonomous reconfiguration and adaptation processes [43].…”

“…IoT communication has been used to perform data transmission using publish/subscribe messaging, e.g. MQTT protocol [43], for event-driven process automation purposes.…”

“…Negotiation based approaches with machine learning have been used for choosing proper routing and transportation of products, e.g. for storing or scrapping [43]. Genetic algorithms have also been used under the scope of ML.…”

Intelligent Systems in Healthcare: An Architecture Proposal

“…Overall, MAS provide an effective method for simulating complex systems and enhancing their behavior. Its appropriateness depends on the particular use case and project objectives, but it has drawn a lot of attention recently as a way to boost coordination and efficiency across a number of industries [12].…”

A Framework for Manufacturing System Reconfiguration Based on Artificial Intelligence and Digital Twin

“…There is a large body of research on automation agent architectures -distributed artificially intelligent programs that collaborate to solve problems -that allow for 'on the fly' reconfiguration of manufacturing systems, exploiting asset flexibility and control to change the behaviour of the manufacturing system [7]. Multi-agent systems and the concept of cyber-physical systems (CPS) were key contributors to research in the reconfiguration of manufacturing environments at the beginning of Industry 4.0 [8,9].…”