Warehouse location method by taking consideration of online dealers in crossing warehouse network

A technology for retailers and warehouses, applied in the field of supply chain, it can solve the problem that the mixed integer optimization model cannot be well extended.

Active Publication Date: 2018-12-07
杭州览众数据科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The current site selection strategies are based on the experience of experts in this field and the mixed integer optimization model that considers different optimization objectives to sol...

Method used

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  • Warehouse location method by taking consideration of online dealers in crossing warehouse network
  • Warehouse location method by taking consideration of online dealers in crossing warehouse network
  • Warehouse location method by taking consideration of online dealers in crossing warehouse network

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Experimental program
Comparison scheme
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Embodiment

[0096] see figure 2 - Figure 4, an embodiment of the invention

[0097] Dataset: 3,488,727 transaction records of Chinese e-commerce company Xiaoye in 2012. More than one million customers from 371 major cities.

[0098] Question: How to assign these customers to warehouses and how to locate the warehouses.

[0099] Step 1: Distribution forecast of customer demand

[0100] To optimize the parameters of the ANN and prevent overfitting, the original 371 cities were randomly divided into three parts. The first part is 60% of the full set for training, the second part is 15% of the full set for cross-validation, and the third part is 15% of the full set for testing. like figure 2 shown, represents the predicted performance of individual parts and the whole. The effectiveness of the ANN network can be seen from the figure.

[0101] Step 2. Warehouse location selection

[0102]For each specified K, first run the E&M algorithm proposed by the present invention to find an op...

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Abstract

The present invention discloses a warehouse location method by taking consideration of online dealers in a crossing warehouse network. The method comprises the following steps of: the step 1, giving acustomer distribution set, related features of client logistics satisfaction and related features of customer purchase ability, and based on the given features and the given set, employing an artificial neural network method to predict a product sales demand in a year in future; and the step 2, solving an optimization warehouse location problem through adoption of an E&M clustering algorithm witha distance weight, and performing reduction of the problem through a hierarchical clustering mode. The warehouse location method can rapidly, accurately and effectively reduce the storage transportation cost, enhance the decision-making ability of enterprises leaders, optimize the enterprise supply chain management, reduce the management cost and improve the industrial competitiveness of enterprises.

Description

technical field [0001] The invention belongs to the field of supply chain, and in particular relates to a warehouse location method for an online retailer in an intersecting warehouse network. Background technique [0002] In recent years, the rise of online marketplaces around the world provides customers who purchase products and services online with the entire process of developing, marketing, selling, delivering, and serving online forms. Indeed, online transactions provide a convenient and efficient way for both customers and retailers. However, online retailers must improve their supply chain network to provide better service. The goal of supply chain management is to deliver the goods in the shortest time and with the lowest cost under the premise of ensuring the best quality of service. In the development of a successful supply chain system, design an effective warehouse location strategy. [0003] The current location selection strategies are based on the experie...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q10/08G06K9/62
CPCG06Q10/043G06Q10/06313G06Q10/087G06F18/231
Inventor 陈灿王一君熊辉刘峻铭吴珊珊
Owner 杭州览众数据科技有限公司
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