On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physi...

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Vydáno v:Applied sciences Ročník 13; číslo 17; s. 9540
Hlavní autoři: Siatras, Vasilis, Bakopoulos, Emmanouil, Mavrothalassitis, Panagiotis, Nikolakis, Nikolaos, Alexopoulos, Kosmas
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2023
Témata:
agent-oriented programming
Algorithms
Anabolic steroids
Artificial intelligence
Asset Administration Shell
Communication
Decision making
Industry 4.0
Interoperability
Manufacturers
Mathematical programming
multi-agent system
Online assets
Optimization
Scheduling
scheduling agent
Supply chains
Germany
ISSN:2076-3417, 2076-3417
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Shrnutí:Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system’s ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent’s information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent’s ability to adapt and provide fast and accurate scheduling results.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13179540