Dynamic Replanning using Multi-Agent Systems and Asset Administration Shells

Jungbluth, Simon; Hermann, Jesko; Motsch, William; Pourjafarian, Monireh; Сидоренко, А. Н.; Volkmann, Magnus; Zoltner, Kuno; Plociennik, Christiane; Ruskowski, Martin

doi:10.1109/etfa52439.2022.9921716

Cited by 12 publications

(3 citation statements)

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“…The SARL agent also has a set of behaviors, which consist of event handlers and event emitters. This enables to implement different interaction protocols, e.g., contract-net-protocol (CNP), which can be used for dynamic planning and resource allocation (Jungbluth et al, 2022 ). The resource agent, together with the CPPM and the AAS, builds up the Resource Holon.…”

Section: Proposed Architecturementioning

confidence: 99%

“…We use the AAS standard model to provide the discovery functionality for the agents. To form the production configuration, one popular approach is to use some auction-based distributed scheduling protocol, e.g., as in Bussmann and Schild (2001), Knabe et al (2002), Leitão andRestivo (2006), andJungbluth et al (2022). Another, bio-inspired planning ahead approach of the Delegate-MAS (DMAS) from Valckenaers (2019), uses lightweight agents, called ants, for looking ahead and evaluating different production possibilities before the actual resource allocation happens.…”

Section: Multi-agent and Holonic Manufacturing Systemsmentioning

confidence: 99%

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The MAS4AI framework for human-centered agile and smart manufacturing

Sidorenko,

Motsch,

van Bekkum

et al. 2023

Front. Artif. Intell.

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Volatility and uncertainty of today's value chains along with the market's demands for low-batch customized products mandate production systems to become smarter and more resilient, dynamically and even autonomously adapting to both external and internal disturbances. Such resilient behavior can be partially enabled by highly interconnected Cyber-Physical Production Systems (CPPS) incorporating advanced Artificial Intelligence (AI) technologies. Multi-agent solutions can provide better planning and control, improving flexibility and responsiveness in production systems. Small modular parts can autonomously take intelligent decisions and react to local events. The main goal of decentralization and interconnectivity is to enable autonomous and cooperative decision-making. Nevertheless, a more efficient orchestration of various AI components and deeper human integration are required. In addition, global behaviors of coalitions of autonomous agents are not easily comprehensible by workers. Furthermore, it is challenging to implement an Industry 4.0 paradigm where a human should be in charge of decision-making and execution. This paper discusses a Multi-Agent System (MAS) where several software agents cooperate with smart workers to enable a dynamic and reconfigurable production paradigm. Asset Administration Shell (AAS) submodels hold smart workers' descriptions in machine-readable format, serving as an integration layer between various system's components. The self-description capability of the AAS supports the system's adaptability and self-configuration. The proposed concept supports the plug-and-produce functionality of the production modules and improves human-machine integration in the shared assembly tasks.

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Section: Proposed Architecturementioning

confidence: 99%

Section: Multi-agent and Holonic Manufacturing Systemsmentioning

confidence: 99%

The MAS4AI framework for human-centered agile and smart manufacturing

Sidorenko,

Motsch,

van Bekkum

et al. 2023

Front. Artif. Intell.

Self Cite

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“…The concept of AAS has been used in many MASs in order to standardize the interaction of the agents [46][47][48][49][50]. Moreover, AASs are a standardized interface for external communication [40,51].…”

Section: Related Workmentioning

confidence: 99%

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

Siatras,

Bakopoulos,

Mavrothalassitis

et al. 2023

Applied Sciences

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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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