Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy

Kai Zhang, Ting Qu, Yongheng Zhang, Ray Y. Zhong and George Huang

International Journal of Production Research, 2022, vol. 60, issue 13, 4159-4175

Abstract: Diversified customer needs make the production system more susceptible to high-frequency fluctuations of uncertain factors (UFs), which puts forward higher requirements for the real-time and systematic decision-making of the system. The opti-state control strategy enables the system to maintain the adaptive optimal state after being disturbed. The core intelligent synchronisation of the opti-state control strategy needs to perceive the state of the affected system and its degree of change. Aiming at the challenge of difficulty in evaluating the uncertain factors impact degree (UFID) of the complex production logistics system, this work proposes a big data-enabled intelligent synchronisation under the opti-state control strategy. Based on the simulation data of system operation, big data is used to mine the relationship between the UFID and the system states, then use wrapper GA-DNN (Deep Neural Network) feature selection and classification method evaluates the UFID, which will be applied to the synchronisation decision. The results show that the method can accurately evaluate the UFID and avoid the waste of resources and the increase in operating costs caused by excessive evaluation of the UFID, thereby also improves the effectiveness and efficiency of the opti-state control strategy.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.2000657 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:60:y:2022:i:13:p:4159-4175

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.2000657

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().