Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Journal article

Yao, F, Alkan, B, Ahmad, B and Harrison, R (2020). Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation. Sensors (Switzerland). 20 (21), pp. 1-25. https://doi.org/10.3390/s20216333
AuthorsYao, F, Alkan, B, Ahmad, B and Harrison, R
Abstract

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.

Keywordsinternet-of-things; flexible manufacturing systems; shop-floor logistics; industry 4.0; autonomous guided vehicles; decision support systems
Year2020
JournalSensors (Switzerland)
Journal citation20 (21), pp. 1-25
PublisherMDPI
ISSN1424-8220
Digital Object Identifier (DOI)https://doi.org/10.3390/s20216333
Publication dates
Print01 Nov 2020
Online06 Nov 2020
Publication process dates
Accepted30 Oct 2020
Deposited15 Dec 2020
Publisher's version
License
File Access Level
Open
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