{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:22:53Z","timestamp":1772774573296,"version":"3.50.1"},"reference-count":33,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>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, which 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.<\/jats:p>","DOI":"10.1142\/s0218194025500949","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T12:52:26Z","timestamp":1761915146000},"page":"803-831","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Approach for Supply Chain Shipment Pricing Prediction Using Temporal Convolutional Network-Residual Neural Network"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6997-3705","authenticated-orcid":false,"given":"Tzu-Chia","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Tamkang University, New Taipei City, 251301, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"key":"S0218194025500949BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3177888"},{"key":"S0218194025500949BIB002","doi-asserted-by":"publisher","DOI":"10.1038\/srep03415"},{"key":"S0218194025500949BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2779181"},{"key":"S0218194025500949BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.irfa.2018.07.010"},{"key":"S0218194025500949BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118604"},{"key":"S0218194025500949BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3135620"},{"key":"S0218194025500949BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.04.053"},{"key":"S0218194025500949BIB008","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140451"},{"key":"S0218194025500949BIB009","doi-asserted-by":"publisher","DOI":"10.38094\/jastt62244"},{"key":"S0218194025500949BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2025.111573"},{"key":"S0218194025500949BIB011","unstructured":"A. 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