基于深度神经网络的物流配送需求预测

2018 7th International Conference on Industrial Technology and Management (ICITM) Pub Date : 2018-03-01 DOI:10.1109/ICITM.2018.8333964

Yao-San Lin, Yaofeng Zhang, I. Lin, Che-Jung Chang

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引用次数: 6

摘要

交货时间影响物流路线，取决于地点和数量的需要。有效的配送需求预测有助于物流模型的构建。配送需求数据具有时间依赖性和空间相关性。对多维序列进行建模或基于它进行预测将是一项消耗计算量的工作。我们的研究是基于深度学习提出一个有效的过程来预测交付需求。通过仿真研究，该方法的预测性能是可以接受的。这有利于物流决策的进一步研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting logistics delivery demand with deep neural networks

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.

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2018 7th International Conference on Industrial Technology and Management (ICITM)

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