Performance analysis and deadlock avoidance for automated guided vehicle systems
The utilisation of Automated Guided Vehicles (AGVs) in the transport and shipment industry is currently an actively explored area. However, there are still various questions raised regarding the optimality of their performance and their ability to avoid deadlock occurrence. This paper focusses on ad...
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2023
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sg-ntu-dr.10356-1661612023-04-21T15:39:27Z Performance analysis and deadlock avoidance for automated guided vehicle systems Aks, Tayal Huang Shell Ying School of Computer Science and Engineering ASSYHUANG@ntu.edu.sg Engineering::Computer science and engineering::Computer systems organization::Performance of systems The utilisation of Automated Guided Vehicles (AGVs) in the transport and shipment industry is currently an actively explored area. However, there are still various questions raised regarding the optimality of their performance and their ability to avoid deadlock occurrence. This paper focusses on addressing these issues by first introducing a discrete-event based zone-control paradigm to model AGV operation. The paper then also propose a traffic-control strategy and routing algorithm to ensure optimal system performance and guaranteed collision-free and deadlock-free operation. Finally, the performance of the proposed system is evaluated using a Finite State Machine-based simulation platform that compares various performance metrics from this model against those obtained from past studies running models in the same environmental conditions, thereby ensuring fair comparison benchmarks. The paper finally concludes with a comprehensive comparison of the studies, along with some possible areas of improvement for future studies to focus on. Bachelor of Engineering (Computer Science) 2023-04-18T01:31:31Z 2023-04-18T01:31:31Z 2023 Final Year Project (FYP) Aks, T. (2023). Performance analysis and deadlock avoidance for automated guided vehicle systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166161 https://hdl.handle.net/10356/166161 en SCSE21-0920 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computer systems organization::Performance of systems Aks, Tayal Performance analysis and deadlock avoidance for automated guided vehicle systems |
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The utilisation of Automated Guided Vehicles (AGVs) in the transport and shipment industry is currently an actively explored area. However, there are still various questions raised regarding the optimality of their performance and their ability to avoid deadlock occurrence. This paper focusses on addressing these issues by first introducing a discrete-event based zone-control paradigm to model AGV operation. The paper then also propose a traffic-control strategy and routing algorithm to ensure optimal system performance and guaranteed collision-free and deadlock-free operation. Finally, the performance of the proposed system is evaluated using a Finite State Machine-based simulation platform that compares various performance metrics from this model against those obtained from past studies running models in the same environmental conditions, thereby ensuring fair comparison benchmarks. The paper finally concludes with a comprehensive comparison of the studies, along with some possible areas of improvement for future studies to focus on. |
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Huang Shell Ying |
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Huang Shell Ying Aks, Tayal |
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Final Year Project |
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Aks, Tayal |
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Aks, Tayal |
title |
Performance analysis and deadlock avoidance for automated guided vehicle systems |
title_short |
Performance analysis and deadlock avoidance for automated guided vehicle systems |
title_full |
Performance analysis and deadlock avoidance for automated guided vehicle systems |
title_fullStr |
Performance analysis and deadlock avoidance for automated guided vehicle systems |
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Performance analysis and deadlock avoidance for automated guided vehicle systems |
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performance analysis and deadlock avoidance for automated guided vehicle systems |
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Nanyang Technological University |
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2023 |
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https://hdl.handle.net/10356/166161 |
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