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A study for further exploring the advantages of using multi-load automated guided vehicles
journal contribution
posted on 2020-08-13, 10:49 authored by Rundong (Derek) Yan, Sarah DunnettSarah Dunnett, Lisa JacksonLisa JacksonMulti-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
Find out more...History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
Journal of Manufacturing SystemsVolume
57Pages
19-30Publisher
ElsevierVersion
- VoR (Version of Record)
Rights holder
© The authorsPublisher 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
2020-08-04Publication date
2020-08-10Copyright date
2020ISSN
0278-6125Publisher version
Language
- en
Depositor
Dr Sarah Dunnett Deposit date: 10 August 2020Usage metrics
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