Hao Dou, Kamel Barkaoui, Hanifa Boucheneb, Xiaoning Jiang and Shouguang Wang
Article (2019)
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Open Access to the full text of this document Published Version Terms of Use: Creative Commons Attribution Download (1MB) |
Abstract
This paper proposes an effective method based on the two main partial order techniques which are persistent sets and covering step graph techniques, to deal with the state explosion problem. First, we introduce a new definition of sound steps, the firing of which enables to extremely reduce the state space. Then, we propose a weaker sufficient condition about how to find the set of sound steps at each current marking. Next, we illustrate the relation between maximal sound steps and persistent sets, and propose a concept of good steps. Based on the maximal sound steps and good steps, a construction algorithm for generating a maximal good step graph (MGSG) of a Petri net (PN) is established. This algorithm first computes the maximal good step at each marking if there exists one, otherwise maximal sound steps are fired at the marking. Furthermore, we have proven that an MGSG can effectively preserve deadlocks of a Petri net. Finally, the change performance evaluation is made to demonstrate the superiority of our proposed method, compared with other related partial order techniques.
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| Department: | Department of Computer Engineering and Software Engineering |
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| Research Center: | Other |
| Funders: | Zhejiang Provincial Key Research and Development Program of China |
| Grant number: | 2018C01084 |
| PolyPublie URL: | https://publications.polymtl.ca/4849/ |
| Journal Title: | IEEE Access (vol. 7) |
| Publisher: | IEEE |
| DOI: | 10.1109/access.2019.2948986 |
| Official URL: | https://doi.org/10.1109/access.2019.2948986 |
| Date Deposited: | 12 May 2021 11:38 |
| Last Modified: | 10 Jan 2026 03:52 |
| Cite in APA 7: | Dou, H., Barkaoui, K., Boucheneb, H., Jiang, X., & Wang, S. (2019). Maximal good step graph methods for reducing the generation of the state space. IEEE Access, 7, 155805-155817. https://doi.org/10.1109/access.2019.2948986 |
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