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Model-based research for aiding decision-making during the design and operation of multi-load automated guided vehicle systems

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journal contribution
posted on 03.12.2021, 09:53 authored by Rundong (Derek) YanRundong (Derek) Yan, Sarah DunnettSarah Dunnett, Lisa JacksonLisa Jackson
Multi-load Automated Guided Vehicle's (AGV) are regarded as a potential tool to tackle the low-efficiency issue that have plagued traditional single-load AGV systems for many years. However, to date, the optimal design and operation of multi-load AGV systems is still an unresolved question. In order to explore the answer to this question and help operators make decisions during the design and operation of these systems, this article will use Coloured Petri nets (CPN) to develop a mathematical model to investigate the performance (i.e., the total number of items delivered within a given time) of the multi-load AGV system in various scenarios. The research has shown that the failure of multi-load AGVs can significantly lower the performance of the AGV system. Although it is possible to maintain high system performance by performing onsite corrective maintenance, the research shows that this can be achieved using a combination of periodic maintenance and backup AGV use. Finally, it is found that increasing the number of multi-load AGVs can increase system performance, but will decrease the efficiency (i.e., the average number of items delivered per AGV) of the individual AGVs in the system due to the increased traffic conflicts and hence longer waiting times.

Funding

Adaptive Informatics for Intelligent Manufacturing (AI2M)

Engineering and Physical Sciences Research Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Reliability Engineering and System Safety

Volume

219

Publisher

Elsevier

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

30/11/2021

Publication date

2021-12-02

Copyright date

2022

ISSN

0951-8320

Language

en

Depositor

Dr Derek Yan. Deposit date: 2 December 2021

Article number

108264