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 2025-12-06
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, Vol. 36, No. 6 (2026) 803–831
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 0218-1940
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