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Novel methodology for optimising the design, operation and maintenance of a multi-AGV system

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  • Yan, Rundong
  • Dunnett, S.J.
  • Jackson, L.M.

Abstract

Automated guided vehicles (AGVs) have long been identified as a potential driver to improve system efficiency and lower labour costs in material handling systems. Accordingly, the reliability and availability of AGV systems is crucial to assure the stability and efficiency of these systems. However, the reliability issues and maintenance strategies of AGVs have not previously been studied sufficiently. This is even more marked in the case of multi-AGV systems that consist of fleets of AGVs. To fill this knowledge gap, research is conducted considering a multi-AGV system, consisting of three AGVs, in order to develop a scientific methodology for optimising the layout design, operation and maintenance of a multi-AGV system. Once an AGV is failed, it will be towed to the maintenance site for repair by a recycle vehicle to prevent deadlock and conflict. The efficiency of the recycling process of failed AGVs in a multi-AGV system, with respect to the change of location of the maintenance site, is analysed by the approach of coloured Petri nets (CPNs). A CPN model simulating the corrective and periodic preventive maintenance processes of failed AGVs is also developed in order to investigate the impact of different AGV maintenance strategies on the operation efficiency of the multi-AGV system. The simulation results obtained clearly show that the location of maintenance sites and maintenance strategies do have significant influence on the performance of a multi-AGV system, where corrective maintenance is an effective measure to maintain the long-term reliability and stability of the system.

Suggested Citation

  • Yan, Rundong & Dunnett, S.J. & Jackson, L.M., 2018. "Novel methodology for optimising the design, operation and maintenance of a multi-AGV system," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 130-139.
    • Handle: RePEc:eee:reensy:v:178:y:2018:i:c:p:130-139
    DOI: 10.1016/j.ress.2018.06.003
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    References listed on IDEAS

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    Cited by:

    1. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Fragapane, Giuseppe & de Koster, René & Sgarbossa, Fabio & Strandhagen, Jan Ola, 2021. "Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda," European Journal of Operational Research, Elsevier, vol. 294(2), pages 405-426.
    3. Yubang Liu & Shouwen Ji & Zengrong Su & Dong Guo, 2019. "Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    4. Yan, R. & Dunnett, S.J. & Jackson, L.M., 2022. "Model-Based Research for Aiding Decision-Making During the Design and Operation of Multi-Load Automated Guided Vehicle Systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Agnieszka A. Tubis & Honorata Poturaj, 2022. "Risk Related to AGV Systems—Open-Access Literature Review," Energies, MDPI, vol. 15(23), pages 1-23, November.

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