Design of scalable agent-based reconfigurable manufacturing systems with Petri nets

ABSTRACT

Reconfigurable manufacturing systems (RMS) provide a flexible paradigm to deal with frequently changing demand and technologies. Scalability is an important property that determines whether the capacity of RMS can accommodate fluctuation in product demand by adjusting/reconfiguring machines and/or structure of production processes. Although there are several studies on scalability of RMS, there are relatively few studies that provide a framework that supports the development of scalable RMS based on multi-agent systems (MAS), from modelling, analysis to design. Motivated by this urgent need, this paper attempts to bridge the gap between theoretical development and design of scalable agent-based RMS. Although MAS and agents’ characteristics of autonomy provide a suitable architecture to model and capture interactions of entities in agent-based manufacturing systems, the original MAS lack process models for agents. By adopting Petri nets as the process models of agents, the capacity scalability problem (CSP) in agent-based RMS can be described by Petri nets in MAS architecture. Due to the lack of optimisation theory developed for Petri nets, the CSP is transformed into an optimisation problem that can be solved by applying classical Lagrange relaxation optimisation theory to relevant agents in the problem-solving processes. Therefore, the methodology proposed to develop scalable agent-based RMS is based on MAS, Petri net models and Lagrange relaxation optimisation theory. The effectiveness of agent-based RMS is illustrated by examples which show that fewer configurable resources are required for agent-based RMS to meet order requirements.

KEYWORDS:

• Flexible manufacturing
• reconfigurable manufacturing systems
• agent-based systems

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST-106-2410-H-324-002-MY2].