Research on the Site Selection Model of Express Service Outlets based on BP Neural Network

Authors

  • Yuan Yuan
  • Jianrong Dong

DOI:

https://doi.org/10.54097/ajmss.v4i3.13178

Keywords:

BP Neural Network, Courier Service, Fuzzy Comprehensive Evaluation

Abstract

With the continuous development of the times, urban planning for the development of the logistics industry puts forward higher requirements, but also for the development of the express industry provides a more potential market, and how to efficiently carry out the site selection of express service outlets has become a difficult problem, so for the current market demand and the current situation. This paper combines the fuzzy comprehensive evaluation method, the establishment of a four-level comprehensive assessment system, the use of BP neural network training, the city express service outlets for scientific site selection and validation analysis, to get the region of the optimal location of each courier service outlets for the city courier service outlets to provide a scientific site selection program.

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References

WU H Y , SHAO D X , NG W S. Locating self-collection points for last-mile logistics using public transport data[J]. Lecture notes in computer science, 2015:498–510.

Wang Bingyi , Zhang Jin . Multilevel Express Network Address Selection Issues Considering Network Sharing and Change [J]. J]. Industrial Engineering,2021,24(02):155-165.

BALLOU R H. Dynamic warehouse location analysis[J]. Journal of marketing research,1968, 5(3): 271-276.

WU H Y , SHAO D X , NG W S. Locating self-collection points for last-mile logistics using public transport data[J]. Lecture notes in computer science, 2015:498–510.

BOSTANCI B , ZEYDAN M , CETE M , et al. Decision making for site selection using fuzzy modeling[J]. Journal of urban planning and development, 2017, 143(1):05016010.

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Published

01-09-2023

Issue

Section

Articles

How to Cite

Yuan, Y., & Dong, J. (2023). Research on the Site Selection Model of Express Service Outlets based on BP Neural Network. Academic Journal of Management and Social Sciences, 4(3), 132-134. https://doi.org/10.54097/ajmss.v4i3.13178