Affiliations: [a] School of Maritime Economics and Management, Dalian Maritime University, Dalian, Liaoning, China
| [b]
Dalian Neusoft University of Information, Dalian, Liaoning, China
Correspondence:
[*]
Corresponding author. Kun Lang, School of Maritime Economics and Management, Dalian Maritime University,
Dalian, Liaoning, China. E-mail: kun.lang@dlmu.edu.cn.
Abstract: In recent years, with the change of people’s living habits, food delivery has become more and more popular. Along with this is the rapid growth of takeaway distribution. It is significant to improve the accurate prediction of the take-out order quantity since it can reduce unnecessary losses for the merchants. The Cloud computing system is the main platform for processing massive data. Applying cloud computing resource scheduling to the forecasting of takeaway orders will greatly help merchants. The research purpose of this paper is to study the cloud computing resource scheduling based on improved ANN model takeaway order volume prediction. This paper improves the ANN model and proposes a cloud computing resource scheduling method based on improved ANN model takeaway order volume prediction. An experimental test was performed to verify the reliability of the proposed method. The results of this study show that the proposed method can accurately predict takeaway orders and contributes to reasonable resource scheduling.
Keywords: Improved ANN model, cloud computing, resource scheduling, takeaway order
volume prediction
