Earticle

초록

영어

Although there have been studies using various algorithms on the delivery time prediction in the logistics business, those studies did not reflect various features such as region or product. In the case of delivery time prediction of a single model that does not reflect the features, the accuracy of delivery time prediction for a region with a high distribution is high, but the prediction accuracy is low for a region with a low distribution. To solve this problem, this paper proposes a method of classifying logistic patterns using clustering and selecting an optimal model for each logistic pattern. The proposed method consists of four steps. First, the derived variables such as reception day, delivery speed and delivery distance are created. Second, the data with the same pattern goes through clustering using K-means. Third, by comparing the performance of each model using six regression algorithms for each classified logistic pattern, an optimal model is selected and the model is stored. Lastly, the logistic pattern of the data to be predicted is classified and the optimal model stored for each pattern is loaded, and the result of delivery time prediction is provided through the model. Two experiments were performed to verify the proposed method. The e-commerce data from Brazil is used as verification data. From the experiment, the delivery time prediction error of the proposed model was smaller than that of the single regression model.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Delivery time prediction
B. Clustering + Regression analysis
III. METERIALS AND METHODS
A. Data integration and pre-processing
B. Data clustering
C. Selecting optimal model and saving the model
D. Loading the optimal model for each logistics pattern and providing prediction results
IV. EXPERIMENTS
A. Performance comparision experiemnt for each model
B. Performance comparision experiment according to the number of clusters
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Deok Ho An [ Department of Computer Engineering Sejong University ]
• So Yeon Woo [ Department of Artificial Intelligence Sejong University ]
• Da Woon Jeong [ Department of Computer Engineering Sejong University ]
• Yeong Hyeon Gu [ Department of Computer Engineering Sejong University ]
• Seong Joon Yoo [ Department of Computer Engineering Sejong University ] Corresponding Author

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