Design and application of Internet of things-based warehouse management system for smart logistics
Open accessAbstract
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.
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Smart transportation enabled by AIoT covers traffic participants, traffic infrastructures, and industry applications.
“Smart transportation enabled by AIoT covers traffic participants (e.g., smart internet of vehicles [256]), traffic infrastructures [257], and industry applications (e.g., smart connected logistics [258]).”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [258]
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“Smart-Logistics [17]) are usually considered a part of Industry 4.”
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“This way, ICT becomes central into what is now popular-ised as the Smart Factory (Wang et al. 2016; Zhong et al. 2017; Strozzi et al. 2017; Yin et al. 2018) and the application of Artificial Intelligence—AI (Lee et al. 2018a).”
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“Host applications poll the data from this server and integrate the fuzzy inference system as well as online JavaScript reports (Lee et al. 2018).”
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- Authors
- Lee, C. K. M., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Wuhan University of Technology (CN)
- The University of Melbourne (AU)
- Corresponding author
- Lv, Yaqiong
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2017-10-27
- Volume
- 56
- Issue
- 8
- Pages
- 2753-2768
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2766877481
- Cited by
- 431
- References
- 32
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 48 · 2023: 88 · 2022: 62 · 2021: 69 · 2020: 59 · 2019: 29 · 2018: 8
- FWCI
- 28.78
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- International Journal of Production Research (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Funders
- Collabroative Reserach (H-ZDAR)
- Collaborative Research (H-ZDAR)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- Internet of Things, warehouse management systems, smart logistics, order picking, order fulfillment, order variability, Industry 4.0, real-time analytics, computational intelligence, warehouse productivity
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