The Library
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
Tools
Tools
Tools
Yao, Fengjia, Alkan, Bugra, Ahmad, Bilal and Harrison, Robert (2020) Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation. Sensors, 20 (21). 6333. doi:10.3390/s20216333 ISSN 1424-8220.
|
PDF
WRAP-improving-just-in-time-delivery-performance-IoT-enabled-flexible-manufacturing-system-Harrison-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (7Mb) | Preview |
Official URL: http://dx.doi.org/10.3390/s20216333
Abstract
Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures |
|||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Automated guided vehicle systems, Automobiles -- Automatic control, Internet of things, Flexible manufacturing systems | |||||||||
| Journal or Publication Title: | Sensors | |||||||||
| Publisher: | MDPI AG | |||||||||
| ISSN: | 1424-8220 | |||||||||
| Official Date: | 6 November 2020 | |||||||||
| Dates: |
|
|||||||||
| Volume: | 20 | |||||||||
| Number: | 21 | |||||||||
| Article Number: | 6333 | |||||||||
| DOI: | 10.3390/s20216333 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 18 November 2020 | |||||||||
| Date of first compliant Open Access: | 19 November 2020 | |||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
