Applications of Petri nets in production scheduling: a review

Tuncel, Gonca; Bayhan, G. Miraç

doi:10.1007/s00170-006-0640-1

Cited by 75 publications

(30 citation statements)

References 58 publications

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“…Manufacturing scheduling is an optimization process, considered as NP-hard problem. Reviews of PN/CPN applications in modeling of FMS and production scheduling have been presented (e.g., [13,17]). Scheduling and control schemes based on combinations of AI techniques and PNs/CPNs aim to explore the advantages of both techniques.…”

Section: Pns and Cpns Combined With Ai Techniques For Manufacturing Smentioning

confidence: 99%

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Drakaki

Tzionas

2017

Applied Sciences

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Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.

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Section: Pns and Cpns Combined With Ai Techniques For Manufacturing Smentioning

confidence: 99%

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Drakaki

Tzionas

2017

Applied Sciences

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“…Meta-heuristics have also been combined with Petri net modelling framework to solve complex scheduling problems [19]. With such an approach, the modelling power of Petri nets can be employed, and relatively good solutions of scheduling problems can be found with a reasonable computational effort, although the convergence to the optimum can not be guaranteed.…”

Section: Petri Net Based Derivation Of Optimal or Sub-optimal Schedulesmentioning

confidence: 99%

Automated Petri-Net Modelling for Batch Production Scheduling

Gradišar¹,

Mušič²

2012

Petri Nets - Manufacturing and Computer Science

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“…PNs have gained the spotlight as a solution for the problem of scheduling and rescheduling in discrete and in continuous and batch processes (Tuncel & Bayhan, 2007), (http://research.curtin.edu.au/, 2010), (Fu-Shiung, n.d.), (Ghaeli et al, 2005), (Tittus & Akesson, 1999), (Tittus & Lennartson, 1997), (Zhou, 1995) and (Tittus et al, n.d.). According to (Tuncel & Bayhan, 2007), PN based methods directly describe the current dynamic behavior and the control system's logic. The most significant advantage of using PNs, is their ability to capture precedence and structural relationships, and to model blockings, conflicts, buffer sizes and multi-resource restrictions.…”

Section: Previous Workmentioning

confidence: 99%