The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT
Open accessAbstract
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems.
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Cites
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Cited by
Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103309 · Cited by 132
Metadata
- Authors
- Zhang, Y., Guo, Z., Lv, J., & Liu, Y.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-09
- Volume
- 14
- Issue
- 9
- Pages
- 4019-4032
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2806682373
- Cited by
- 292
- References
- 40
- FWCI
- 29.49
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, Y., Guo, Z., Lv, J., & Liu, Y. (2018). A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2018.2845683
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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