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Open Access | Online First

Length-Bounded Least-Cost Transition Firing Sequence Estimation in Petri Nets with Unobservable Transitions

School of Information Engineering, Yangzhou University, Yangzhou 225127, China
School of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China, also with School of Computer Science and Engineering, College of Engineering, Nanyang Technological University, Singapore 639798
COSMO Industrial Intelligence Institute (Qingdao) Co. Ltd., Qingdao 266103, China, also with State Key Laboratory of Massive Personalized Customization System and Technology, Qingdao 266100, China
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Abstract

This work studies the problem of estimating the Transition Firing Sequences (TFSs) for Peri nets, where each transition is supposed to be either observable or unobservable. Due to the existence of cycles of unobservable transitions, the number of transition firing sequences may potentially be infinite. In this paper, based on the notion of minimal explanation, we can find a finite number of TFSs for which the number of observable transitions is less than or equal to a given bound. In particular, we assume that the initial and target markings are known. We propose a backtracking algorithm to find the least-cost transition firing sequences that will lead to the target state from the initial one. Moreover, two heuristic methods are proposed to reduce computational effort further. A wireless sensor network system example is provided to illustrate the proposed approaches. According to the example and comparative analysis, we verify that the proposed method can achieve the maximum number of least-cost path solutions. Finally, we present a survey and some comparative studies of various approaches in the relevant literature.

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Cite this article:
Wang M, Bian H, Pang S, et al. Length-Bounded Least-Cost Transition Firing Sequence Estimation in Petri Nets with Unobservable Transitions. Tsinghua Science and Technology, 2025, https://doi.org/10.26599/TST.2025.9010007

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Received: 17 September 2024
Revised: 17 December 2024
Accepted: 10 February 2025
Published: 26 September 2025
© The author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).