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Journal Article
Efficient Weighted Ensemble Method for Predicting Peak-Period Postal Logistics Volume: A South Korean Case Study
- Authors
- Eunhye Kim, Tsatsral Amarbayasgalan, Hoon Jung
- Issue Date
- 2022-12
- Citation
- Applied Sciences, v.12, no.23, pp.1-15
- ISSN
- 2076-3417
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- Case studies, Demand prediction, Ensemble method, Internal and external, Logistic operations, Logistics service, Second phase, South Korea, Unsupervised learning, Weighted Average, Weighted ensemble
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
