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자기 유사성 기반 소포우편 단기 물동량 예측모형 연구
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- Issue Date
- 2020-12
- Citation
- 산업경영시스템학회지, v.43, no.4, pp.76-83
- ISSN
- 2005-0461
- Publisher
- 한국산업경영시스템학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.
- KSP Keywords
- Load analysis, Logistics operation, Multiple Regression, Prediction accuracy, Traffic Prediction, Traffic forecasting, feature engineering, load scheduling, logistics system, prediction model, prediction performance