期刊
APPLIED SCIENCES-BASEL
卷 12, 期 23, 页码 -出版社
MDPI
DOI: 10.3390/app122311962
关键词
weighted ensemble method; postal logistics volume; peak-period prediction; unsupervised learning
类别
资金
- Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea government (MSIT)
- [2018-0-01664]
This study proposes a Multilayer Perceptron-based weighted ensemble method for predicting the accepted parcel volumes during special periods. The experimental study on the dataset provided by Korea Post shows better performance than other compared methods.
Demand prediction for postal delivery services is useful for managing logistic operations optimally. Particularly for holiday periods, namely the Lunar New Year and Korean Thanksgiving Day (Chuseok) in South Korea, the logistics service increases sharply compared with the usual period, which makes it hard to provide reliable operation in mail centers. This study proposes a Multilayer Perceptron-based weighted ensemble method for predicting the accepted parcel volumes during special periods. The proposed method consists of two main phases: the first phase enriches the training dataset via synthetic samples using unsupervised learning; the second phase builds two Multilayer Perceptron models using internal and external factor-derived features for prediction. The final result is estimated by the weighted average predictions of these models. We conducted experiments on 25 Korean mail center datasets. The experimental study on the dataset provided by Korea Post shows better performance than other compared methods.
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