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Optimizing the Scheduling of Autonomous Guided Vehicle in a Manufacturing Process
Abstract
Autonomous Guided Vehicles (AGVs) are considered as one of the key enablers of smart factories which make possible smart and flexible transportation of pallets and material on shopfloor. However, existing AGV fleet management solutions often suffer from poor integration with real-time manufacturing operations information systems, which negatively affects scheduling of AGVs. To exploit the full potential of AGVs in achieving just-intime (JIT) transportation, there is a need for intelligent AGV fleet management system which not only integrate with manufacturing information technology (IT) and operational technology (OT) but also provide prediction for the shop-floor logistic based on real-time manufacturing operations information to optimize scheduling of AGVs. This paper presents an approach for a Smart AGV Management System (SAMS), which combines the real-time data analysis and digital twin models that can be deployed within complex manufacturing environments for optimized scheduling. For a proof of concept, a case study of a line side supply of components to a manual assembly station is presented.
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In alignment with machine states found in literature, the identified states in the stations of the laboratory are Idle, Working, Energy-saving mode, and Fault.
“In alignment with the machine states found in literature, the identified states in the stations of the laboratory are [81]:”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [81]
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References 11 · Cited by 53
Research works printed but not linked: 21
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Metadata
- Authors
- Yao, F., Keller, A., Ahmad, M., Ahmad, B., Harrison, R., & Colombo, A. W.
- Affiliations
- University of Warwick (GB)
- ORCID
- Venue
- 2018 IEEE 16th International Conference on Industrial Informatics (INDIN)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 264-269
- ISSN
- 1935-4576 (ISSN-L)
- OpenAlex
- W2894225858
- Cited by
- 53
- References
- 11
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 9 · 2022: 10 · 2021: 10 · 2020: 9 · 2019: 5
- FWCI
- 9.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Industrial Informatics (INDIN) (publishedVersion)
- Hochschulschriftenserver der Hochschule Emden/Leer (submittedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/K018191/1)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Scheduling and Optimization Algorithms
- Keywords
- autonomous guided vehicles, AGV scheduling, smart factory, digital twin, manufacturing information systems
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