Predicting Logistics Delivery Demand with Deep Neural Networks

被引：0
|
作者：
Lin, Yao-San [1 ]
Zhang, Yaofeng [1 ]
Lin, I-Ching [2 ]
Chang, Che-Jung [3 ]
机构：
[1] Hubei Univ Econ, Collaborat Innovat Ctr China Pilot Reform, Explorat & Assessment Hubei Subctr, Wuhan, Peoples R China
[2] Hubei Univ Econ, Sch Logist & Engn Management, Wuhan, Peoples R China
[3] Ningbo Univ, Business Sch, Dept Management Sci & Engn, Ningbo, Peoples R China
来源：
2018 7TH INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY AND MANAGEMENT (ICITM 2018) | 2018年
关键词：
deep learning; delivery prediction; logistic; LSTM; ANALYTICS; SETS;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Delivery time affects the logistics route, depending on the needs of the place and quantity. An efficient prediction of delivery demand would help the construction of logistics model. The data on delivery demand are time-dependency and space-correlation. Modeling the multidimensional sequence or making the prediction based on it would be a computation consuming work. Our research is based on deep learning to propose an efficient procedure to predict delivery demand. With the simulation study, the prediction performance of the proposed procedure is acceptable. This is conducive to the further study of logistics decisions making.
引用
收藏
页码：294 / 297
页数：4
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