An agent-based self-configuration methodology for modular assembly systems
Publication Date
2011
Creator
Abstract
Assembly systems today are exposed to market trends that have become increasingly more dynamic and unpredictable, requiring product changes and adjustments which emphasise de need for more flexible systems. The requirement for increased responsiveness has led to the development of new modular concepts which provide the bases for achieving higher system adaptability through increased component/module interchangeability and reusability. The modularization of physical and control infrastructure does, however, only address one aspect of the issue and there is still a lack of appropriate tools and methods to support the rapid configuration and reconfiguration of such systems for changing sets of requirements.
This work proposes a new distributed methodology for the configuration and reconfiguration of Modular Assembly Systems (MAS) through the use of agent technology. The new methodology defines a comprehensive model for the structured description of the MAS requirements, equipment modules and the configuration results.
This thesis proposes a new agent architecture for the self-configuration of equipment modules into systems based on a given set of requirements, as the core of the self-configuration methodology. This architecture introduces the overall behaviour of the methodology through the definition of agent types, roles and overall interactions. Furthermore this work describes the development of the specific models and methods for the local behaviour of each agent. These enable the actual decision making method for the agents to achieve configuration solutions.
This work also reports on a new methodology for the early performance simulation of MAS characteristics that can be used in conjunction with the configuration methodology.
Item Type
ethesis
Thesis Type
PhD
Supervisors
Subjects (LC)
Associated Schools / Departments
Department of Mechanical, Materials and Manufacturing Engineering (UK)
eprints ID
12325
UoN Repository URI
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