Deep Learning-Based Demand Forecasting for Korean Postal Delivery Service

Abstract

Proper demand forecasting for postal delivery service can be used for optimal logistic management, staff scheduling and topology planning. More especially, during special holidays, such as the Lunar New Year and the Chuseok (Mid-autumn day), the demand for delivery service increases sharply in South Korea. It makes a challenge to forecast demand to provide a normal delivery schedule for the Korean mail center. To address this problem, we propose a novel deep learning model equipped with selection and update layers (MLP-SUL) to achieve high predictive performance. The proposed model consists of three main parts: the first part of the model learns to generate context-dependent weights to decide which input feed to the next layer; the second part updates the weighted input to prepare encoded input, and the third part is a prediction layer that consists of a linear layer. A linear layer takes encoded input for forecasting demand. We also introduce a special data preprocessing step for our task that requires long-term forecasting. The experimental results show that our proposed deep learning model outperforms state-of-the-art baselines on Korean mail center datasets.