A novel approach for Supply Chain Shipment Pricing Prediction using Temporal Convolutional Network- Residual Neural Network
學年 114
學期 1
出版（發表）日期 2025-10-31
作品名稱 A novel approach for Supply Chain Shipment Pricing Prediction using Temporal Convolutional Network- Residual Neural Network
作品名稱（其他語言）
著者 Tzu-Chia Chen
單位
出版者
著錄名稱、卷期、頁數 International Journal of Software Engineering and Knowledge
摘要 The supply chain comprises an interconnected system of warehouses, suppliers, shipping companies, distribution hubs, carriers, and logistics firms collaborating to facilitate the progression and commercialization of a product until its final handover to the ultimate consumer. Moreover, efficiently managing overseas supply chains necessitates precise forecasting of shipping times, as it is a serious aspect of operations and advanced information systems. Nonetheless, the feasibility of generating real-time Global Positioning System data and employing optimization methods for short-term and long-term shipping prediction remains an important challenge. Thus, this study develops a novel approach for the supply chain shipment pricing prediction using a hybrid deep learning approach. At first, pre-processing is executed by data normalization and data transformation. Subsequently, feature fusion is performed by Atkinson index and Double Exponential Dung beetle Optimizer (DEDBO) algorithm, that is a combination of Double Exponential Smoothing (DES) and Dung beetle Optimizer (DBO). Ultimately, supply chain shipment prediction is executed by employing the Temporal Convolutional Network- Residual Neural Network (TCN-RNN), which is a combination of TCN and RNN models. The experimentation evaluation shows that DEDBO-based TCN-RNN attains minimal MSE, RMSE, MAE and MAPE with values of 0.0001, 0.0104, 0.0054 and 0.329.
關鍵字
語言 en
ISSN
期刊性質 國內
收錄於 SCI
產學合作
通訊作者 Tzu-Chia Chen
審稿制度 否
國別 SGP
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128198 )

SDGS 優質教育,產業創新與基礎設施