Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Drakaki, Maria; Tzionas, Panagiotis

doi:10.3390/app7020136

Cited by 39 publications

(18 citation statements)

References 57 publications

(95 reference statements)

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“…In [73], an analytic hierarchy process (AHP) based method was proposed to evaluate manufacturing sustainability performance. Moreover, in [74], a novel method with the integration of Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL) was proposed to solve the problem of manufacturing scheduling. Table II summarizes data analytics methods used for MIoT.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…Figure 5(a) shows that the system framework consists of a production line, industrial devices and computing units. In particular, the production line consists of various manufacturing devices, instruments, sensors, actuators and robot arms, all of which are connected through wired or wireless links consequently [74] forming the MIoT. In addition to the production line and industrial devices, there are a number computing units supporting diverse data processing tasks.…”

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Dai

Wang

et al. 2019

Enterprise Information Systems

233

137

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Section: A Data Acquisitionmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Dai

Wang

et al. 2019

Enterprise Information Systems

233

137

View full text Add to dashboard Cite

show abstract

“…From among known such methods, the Petri net, which is a wide and solid discrete event modelling tool, is used in this study. It is a graphical and mathematical modelling tool for discrete event processes used to describe and analyse systems (Murata, 1989;Başak and Albayrak, 2015;Drakaki and Tzionas, 2017). The Petri net is a 5-tuple, i.e.,…”

Section: Mapping and Modellingmentioning

confidence: 99%

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Campos¹,

Ferreira²,

Freires³

2021

BJO&PM

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Goal: This research aims to examine the trade-off behaviour between productivity, quality and maintenance performance and evaluate the efficiency of the framework integrated to a time variability in operational management. Design / Methodology / Approach: The proposed framework integrates a stochastic analysis of process and TVM to manage process variability and analyse the trade-off behaviour between quality, productivity, and maintenance efficiency. The framework generates a control chart for each workstation and analyses the operator's time to execute an assembly process for six workstations. The processes are characterised and evaluated via stochastic simulation, resulting in a trade-off analysis with a multicriteria approach. Results: A relational graphic of performance dimensions shows that productivity and maintenance performance have a high dependency on time variability management, whereas quality has a minor impact over trade-off decisions. The proposed framework has a huge significance to operational management which allow to apply complex models to time variability analysis and organisation performance. Limitations of the investigation: The trade-off analysis in this study is limited to a constant trade-off model to validate the framework. Practical implications: This framework, used in association with traditional management philosophies, is a powerful tool that permits managers to develop a trade-off analysis and make decisions on workstations. Thus, a research field that correlates time variability with performance dimensions may be validated by this framework. Originality / Value: A framework that allows the use of MCDM to analyse the trade-off behaviour on organization performance and a trade-off analysis between performance dimensions and time variability management in operational performance.

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“…The branch and bound approach proved its effectiveness in a single-armed cluster tool when compared with a general mixed integer programming heuristic. In [10], Drakaki et al used Timed Colored Petri Nets (TCPN) and reinforcement learning to model a manufacturing system and to implement the scheduling, in a way to respond to and accommodate environmental changes efficiently. Also under the framework of TCPN, [11] implemented a simulation experiment of a self-adaptive collaboration of production-logistics systems, aiming at validating the performance and the applicability of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Improving Synchronous Dataflow Analysis Supported by Petri Net Mappings

2018

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Whereas most of the work that analyses Synchronous Dataflow (SDF) stays in the dataflow framework, this work pushes its analysis into another framework level, thereby addressing issues that are not well addressed or are even unexplored in SDF. In this manner, the paper proposes a model-driven engineering (MDE) method, combining Synchronous Dataflow (SDF) and Petri nets, to highlight and reinforce their interoperability in digital signal processing applications, cyber-physical systems, or industrial applications. Improvements regarding the settlement and exploitation of the initial conditions associated with SDF are demonstrated; this issue is crucial for every cyber-physical system, since a system’s initial conditions are crucial to ensuring the system’s liveness. The improvements outlined in this work exploit an innovating mapping in the Place/Transition (P/T) Petri net domain that is intended to reduce and predict the total amount of initial data in SDF channels. The relevance of the firing semantics engaged with the equivalent Petri net model is discussed. This paper proposes a new approach to estimate whether an SDF has a static schedule by performing simulation and property verification of the equivalent-based P/T Petri net system achieved, framed by a Petri net invariant analysis and based on the stubborn set method of Petri nets. In this way, this new approach will allow mitigating the state explosion problem. Finally, a strategy is applied to two case studies to discover all the elementary circuits (static schedules) associated with the generated model’s state-space.

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Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Cited by 39 publications

References 57 publications

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Improving Synchronous Dataflow Analysis Supported by Petri Net Mappings

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