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학술지 Efficient Weighted Ensemble Method for Predicting Peak-Period Postal Logistics Volume: A South Korean Case Study
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저자
김은혜, 차츠랄, 정훈
발행일
202212
출처
Applied Sciences, v.12 no.23, pp.1-15
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app122311962
협약과제
22HR3300, 우편물류 인프라 기술개발, 정훈
초록
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.
KSP 제안 키워드
Case studies, Demand prediction, Ensemble method, Experimental study, Internal and external, Logistic operations, Logistics service, Second phase, South Korea, Weighted average, Weighted ensemble
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