Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/73807
| Title: | Design and application of Internet of things-based warehouse management system for smart logistics | Authors: | Lee, CKM Lv, Y Ng, KKH Ho, W Choy, KL |
Issue Date: | 2018 | Source: | International journal of production research, 2018, v. 56, no. 8, p. 2753-2768 | Abstract: | Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability. | Keywords: | Industry 4.0 Internet of things Low–volume, high-mix Smart logistics Warehouse management system |
Publisher: | Taylor & Francis | Journal: | International journal of production research | ISSN: | 0020-7543 | EISSN: | 1366-588X | DOI: | 10.1080/00207543.2017.1394592 | Rights: | © 2017 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 27 Oct 2017 (Published online), available online: http://www.tandfonline.com/10.1080/00207543.2017.1394592. |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| a0768-n06_1567.pdf | Pre-Published version | 1.81 MB | Adobe PDF | View/Open |
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