Daily short-term express delivery business volume prediction method for logistics enterprises

A forecasting method and business volume technology, applied in forecasting, logistics, biological neural network models, etc., can solve problems such as reducing user satisfaction, increasing costs of logistics companies, and difficulty in determining the network structure

Inactive Publication Date: 2018-07-17
ANHUI UNIVERSITY
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Problems solved by technology

However, it has shortcomings such as easy to fall into local optimum, slow convergence speed and difficult to determine the network structure, so overcoming these shortcomings is an extremely important issue.
On the other hand, predicting the

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  • Daily short-term express delivery business volume prediction method for logistics enterprises
  • Daily short-term express delivery business volume prediction method for logistics enterprises
  • Daily short-term express delivery business volume prediction method for logistics enterprises

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Embodiment Construction

[0092] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0093] The application principle of the present invention will be further described below in conjunction with the accompanying drawings.

[0094] Such as figure 1 , a daily short-term express business volume forecasting system for a logistics enterprise provided by an embodiment of the present invention, the system includes three modules:

[0095] Data Selection Module: Assume one hour as a fixed step size. In the experiment, the express business volume within the same step length in the data set is combined into a subset, and 48 subsets can be constructed. Assume that the selected starting point in a sample is i, and the ...

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Abstract

The invention belongs to the technical field of logistics express delivery business volume prediction, discloses a daily short-term express delivery business volume prediction method for logistics enterprises, uses a particle swarm optimization algorithm with an improved inertia weight to optimize the back propagation neural network, and adopts a new horizontal data selection method to select andinput data for the BP neural network. The optimized BP neural network is used to predict the daily short-term express delivery business volume of the logistics company, a proper amount of cloud computing resources can be dynamically applied in different time periods to process the express parcel data and monitor the parcel transportation process. The method can predict the daily short-term expressdelivery business volume, apply for a proper amount of cloud resources, and can process the business data of all express parcels on time without causing waste of cloudy resources. The method can be applied to the daily short-term express delivery business volume forecast of the logistics company, and is of great significance for reducing the cost of the logistics company and improving the qualityof the user service.

Description

technical field [0001] The invention belongs to the technical field of forecasting business volume of logistics and express delivery, and in particular relates to a method for forecasting daily short-term express business volume of a logistics enterprise. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Predicting the future express business volume based on the historical data of logistics express companies is a kind of time series forecasting, and the current backpropagation (BP) neural network has a wide range of applications in time series forecasting. BP neural network is a single feed-forward network with an error back propagation mechanism, which has strong nonlinear, self-learning, self-adaptive, generalization and other capabilities. However, it has shortcomings such as easy to fall into local optimum, slow convergence speed and difficult to determine the network structure, so overcoming thes...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/08G06N3/04
CPCG06Q10/04G06Q10/083G06N3/045
Inventor 许荣斌王业国谢莹郑春厚汪梦龙
Owner ANHUI UNIVERSITY
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