자기 유사성 기반 소포우편 단기 물동량 예측모형 연구
Short-Term Prediction Model of Postal Parcel Trafficbased on Self-Similarity
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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%.
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%.
참고문헌 (20)
- Breiman, L., "Random forests", Machine Learning, 45(1), pp.5~32 (2001)
- 차병철, "중장기 우편물류 네트워크 설계 시스템 개발에 관한 연구", 대한설비관리학회지, 19(1), pp.81~91 (2014), 대한설비관리학회
- Ebbesson, M., "Mail volume forecasting an evaluation of machine learning models", UPTEC IT (2016), Uppsala Uni- versity
- Development of Prediction Technology for Parcel Acceptance and Sorting, "ETRI", ~ (2019)
- Giorgino, T., "Computing and Visualizing Dynamic Time Warping Alignments in R", Journal of Statistical Soft-ware, 31, pp.1~25 (2009)
- Hardle, W.K., "Applied multivariate stati- stical analysis" (2012), Springer
- Hill, T., "Neural Network Models for Time Series Forecasts", Management Science, 42(7), pp.1082~1092 (1996)
- 김태영, "중장기 우편물류를 위한 수요예측 시스템 개발", 한국SCM학회지, 10(2), pp.73~80 (2010), 한국SCM학회
- Kleinbaum, D. G., "Applied regression analysis and other multivariable methods" (2007), Duxbury Press
- Kohonen, T., "Essentials of the Self-Organizing Map", Neural Networks, 37, pp.52~65 (2013)
- Kohonen, T., "Self-organizing map" (2012), Springer-Verlag
- Lee, D.J., "Demand Forecasting of Direct Mail Considering the Changes of Market Environments", Proceedings of Conference of the Korean Operations Research and Management Science Society, pp.776~785 (2005)
- Lee, S.J., "Fore- casting the Letter Volume with Logistic Curve", Pro- ceedings of Conference of the Korean Institute of Indu- strial Engineers, pp.308~312 (2003)
- Lim, J.M., "Forecasting the Mail Volume in Korea", Proceedings of Conference of the Korean Operations Research and Management Science Society, pp.3~6 (2003)
- Muller, M., "Dynamic time warping" (2012), Springer
- Noh, S. J., "A Study on the Parcel Warehouse & Distribution Center Network", IE Interfaces, 16(4), pp.411~420 (2003)
- 박흥수, "수요예측 데이터 분석에 기반한 안전재고 방법론의 현장 적용 및 효과", 한국산업경영시스템학회지, 43(3), pp.61~67 (2020), 한국산업경영시스템학회[DOI]
- 이영, "부품서비스 관점에서 분배 알고리즘을 적용한 수요예측 엔진의 설계 및 개발에 관한 연구", 한국산업경영시스템학회지, 34(4), pp.169~178 (2011), 한국산업경영시스템학회
- Trinkner, U., "Progress toward Liberalization of the Postal and Delivery Sector", pp.267~280 (2006), Springer
- Vu, K.M., "The ARIMA and VARIMA time series :Their modelings, analyses and applications" (2007), AuLac Tech- nologies Inc.