Publication Type
Journal Article
Version
submittedVersion
Publication Date
6-2009
Abstract
Reconfigurable manufacturing systems (RMSs) have been acknowledged as a promising means of providing manufacturing companies with the required production capacities and capabilities. This is accomplished through reconfiguring system elements over time for a diverse set of individualised products often required in small quantities and with short delivery lead times. Recognising the importance of dynamic modelling and visualisation in decision-making support in RMSs and the limitations of current research, we propose in this paper to model RMSs with Petri net (PN) techniques with focus on the process of reconfiguring system elements while considering constraints and system performance. In view of the modelling challenges, including variety handling, production variation accommodation, machine selection, and constraint satisfaction, we develop a new formalism of coloured timed PNs. In conjunction with coloured tokens and timing in coloured and timed PNs, we also define a reconfiguration mechanism to meet modelling challenges. An application case from an electronics company producing mobile phone vibration motors is presented. Also reported are system analysis and application results, which show how the proposed formalism can be used in the reconfiguration decision making process.
Keywords
reconfigurable manufacturing systems, coloured PNs, timed PNs, reconfiguration mechanism
Discipline
Operations and Supply Chain Management
Research Areas
Operations Management
Publication
International Journal of Production Research
Volume
47
Issue
16
First Page
4569
Last Page
4591
ISSN
0020-7543
Identifier
10.1080/00207540801946662
Publisher
Taylor and Francis
Citation
ZHANG, Lianfeng and RODRIGUES, Brian.
Modelling reconfigurable manufacturing systems with coloured timed Petri nets. (2009). International Journal of Production Research. 47, (16), 4569-4591.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/1823
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/00207540801946662