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A study for further exploring the advantages of using multi-load automated guided vehicles

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journal contribution
posted on 2020-08-13, 10:49 authored by Rundong (Derek) Yan, Sarah DunnettSarah Dunnett, Lisa JacksonLisa Jackson
Multi-load Automated Guided Vehicle (AGV) has been proposed for a while, but the advantage of its application is still not fully understood today. In order to fill this knowledge gap, a novel integrated model of a multi-load AGV system is developed in this paper by using an advanced form of Petri Nets, namely Coloured Petri Nets (CPNs), to simulate the operation of the AGV system in various scenarios. The study reported in this paper is focused to answer a few key questions, i.e. whether system performance can be continuously improved by increasing the load capacity of the multi-load AGVs; if not, whether there is an optimal load capacity of the multi-load AGV for a particular system; and whether the multi-load AGVs can still work well in a system with flexible loading and unloading points. The research results have shown that the efficiency of the AGV system can be improved by increasing the load capacity at the beginning, but the effectiveness of such an approach will decrease when the load capacity increases above a certain value. In other words, an AGV system may not perform better after using a larger capacity of multi-load AGV and there must be an optimal load capacity of the multi-load AGV for a specific AGV system. In addition, it is found that a system with flexible loading and unloading points can perform better after using a multi-load AGV.

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

Journal of Manufacturing Systems

Volume

57

Pages

19-30

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 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2020-08-04

Publication date

2020-08-10

Copyright date

2020

ISSN

0278-6125

Language

  • en

Depositor

Dr Sarah Dunnett Deposit date: 10 August 2020

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