https://scholars.lib.ntu.edu.tw/handle/123456789/612193
標題: | Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach | 作者: | Ren S. TSAN MING CHOI Lee K.-M. Lin L. |
關鍵字: | Cross-border e-commerce; Deep learning; Logistics service capacity (LSC) allocation; Multi-product newsvendor; Third-party forwarding logistics (3PFL) | 公開日期: | 2020 | 出版社: | Elsevier Ltd | 卷: | 134 | 來源出版物: | Transportation Research Part E: Logistics and Transportation Review | 摘要: | With the rise of ?ross-border-e-commerce?? the third-party-forwarding-logistics (3PFL) service becomes increasingly popular. Different from the traditional third-party-logistics (3PL) service, the 3PFL company provides forwarding services cost-effectively by consolidating orders from different e-tailers/platforms. The random arrivals of orders create a big challenge. Different from most of the existing studies, a deep learning based one-step integration optimal decision making approach S2SCL(Seq2Seq based CNN-LSTM) is proposed in this paper which intelligently integrates inventory optimization and demand-forecasting process. The Seq2Seq based forecasting architecture, which integrates CNN and LSTM network, is able to model the system dynamics and dependency-relations in varying demand for logistics services. Besides generating the point forecasting results, the proposed approach can quantify demand uncertainty via a dynamic distribution and make optimal decision on logistics service capacity allocation. Through a case-study analysis with real data obtained from a 3PFL company in China's Great Bay Area, we compare the proposed S2SCL with two benchmark models, including a one-step statistics based integration approach ARIMA and a two-step optimization based approach PSO-ELM, for two tasks: (1) point forecasting and (2) optimal logistic service capacity (LSC) allocation. Experimental results show that S2SCL outperforms the two benchmark models in both tasks significantly. ? 2020 Elsevier Ltd |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078560803&doi=10.1016%2fj.tre.2019.101834&partnerID=40&md5=7f32d12044d28ed13a877c1cb9d10136 https://scholars.lib.ntu.edu.tw/handle/123456789/612193 |
DOI: | 10.1016/j.tre.2019.101834 | SDG/關鍵字: | artificial neural network; benchmarking; electronic commerce; forecasting method; logistics; optimization; travel demand; China |
顯示於: | 工商管理學系 |
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