Multi-agent systems to control, simulate, and optimize resilient smart factories
Creators: name: Braun, Sebastian
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 460202
subject: Autonomous agents and multiagent systems
classificationCode: 460603
subject: Cyberphysical systems and internet of things

description: In 2012, "Industry 4.0"' was introduced by the German government to establish Germany as the leading supplier of cyber-physical systems. The Industry 4.0 strategy includes implementation recommendations for future technologies and promotions for avant-garde innovations. Industry 4.0 does not simply provide solutions but declares problem fields, generating a long-term migration strategy toward a homogeneous, reliable, and optimized system landscape. One of these fields is a resilient smart factory - which offers service-oriented manufacturing, implements high-availability concepts, and optimizes itself depending on the product range.

However, these new concepts and optimizations require introducing disruptive technologies into production environments. 

One promising technology are multi-agent systems. In multi-agent systems, assets are represented by software programs, so-called agents. Multi-agent systems can solve complex problems that are difficult or impossible to be described by utilizing a monolithic system. Each factory's component (cyber-physical production systems, machines, transportation, workpieces, storages, etc.) is abstracted with its agent and connected to it by a one-to-one relationship. Components communicate with each other over their agents, creating a non-linear and distributed network describing the current state of the factory. This description of a factory is used to describe dynamic connections between machines and to investigate the benefits and drawbacks of loosely coupled manufacturing. However, the agent's actual performances in production environments can hardly be quantified as most research has focused on isolated projects and specific scenarios.

This thesis focuses on developing a multi-agent framework, applying multi-agent systems for small and medium-sized enterprises with a growing heterogeneous environment with constrained resources, and analyzing the performance of the multi-agent approach in order scheduling. Additionally, a security concept compatible with IEC 62443 is provided because Industry 4.0 connects previously isolated systems, which increases the attack surface to cyberattacks. In the developed framework, the machines take care of the execution of their abilities; the business logic is developed with agile methods on the loosely coupled agent. This approach differs from the trend of automation manufacturers to include business logic in the programmable logic controller. It generates a clear separation between machines and services, leading toward a migration strategy for resilient factories where machines and resources are loosely coupled.

This separation leads to higher modularity, exchangeability, and vendor independency. A system is resilient if it can adjust its functioning before, during, or following events, thereby sustaining required operations under both expected and unexpected conditions.

The specified and developed framework is applied to control a modular production plant and manual workstations at the model factory at the University of Applied Sciences in Aachen. In these use cases, the agents are responsible for the coordination of logistics as well as production execution. Additionally, a highly connected and configurable reference model with quantifiable key performance indicators for production scheduling and routing in single-piece workflows is proposed to evaluate the multi-agents system further. The developed model is used in discrete event simulations to evaluate the multi-agent system's potential, drawbacks, and feasibility in manufacturing environments.

The multi-agent framework leads a path towards an environment that does not require much maintenance but provides a secure and plug-and-produce capable system that can be easily integrated into existing production systems. The multi-agent system is implemented and integrated into the model factory. Field tests show that the developed framework works without problems in small deployments. Furthermore, the framework's scalability is shown in the field tests, although a real workload might show bottlenecks.

Additionally, the conducted simulations show the flexibility of multi-agent systems and highlighted that such systems can emerge behaviors to fulfill agendas in production environments. Simulation results highlight that the adaptive natures of the proposed multi-agent approach make them highly suitable for improving the performance of systems with sub-optimum configurations or control strategies. Nonetheless, the increased flexibility allows the multi-agent system to find alternative solutions paths and deliver improved factory-planning outcomes in systems with failures.