Deep Learning Algorithm Aided E-Commerce Logistics Node Layout Optimization Based on Internet of Things Network

Lifeng Li

doi:10.4108/eetsis.v10i3.3089

Authors

Lifeng Li Shenyang polytechnic college, Liaoning, China

DOI:

https://doi.org/10.4108/eetsis.v10i3.3089

Keywords:

IoT Big Data, Deep Learning Algorithm, E-commerce Logistics, Logistics Node Layout and Optimization, Improved Logistics Node Layout Scheme

Abstract

INTRODUCTION: In recent years, e-commerce has shown a booming trend. Influenced by e-commerce, people's logistics needs have also increased sharply in recent years.

OBJECTIVES: Research on the node layout and optimization of e-commerce logistics is conducive to improving the scientificity and rationality of logistics node layout, improving logistics distribution efficiency, reducing logistics distribution costs, and better meeting consumers' logistics needs. However, due to the unreasonable layout of logistics nodes in some areas, it has brought huge logistics cost investment to e-commerce companies, and also laid hidden dangers for the long-term development of e-commerce companies.

METHODS: Based on this, this paper studied the node layout and optimization of e-commerce logistics by using IoT big data and deep learning algorithms, and proposed an improved logistics node layout scheme based on IoT big data and deep learning algorithms. The experimental research was carried out from five aspects: the transportation cost of logistics, the efficiency of logistics distribution, the accuracy of logistics information transmission, the location and traffic conditions of logistics nodes, and the evaluation of the plan by e-commerce enterprises.

RESULTS: The research results showed that the improved logistics node layout scheme can improve the efficiency of logistics distribution by 3.69% and the accuracy of logistics information transmission by 4.34%, and can reduce the logistics transportation cost of e-commerce enterprises.

CONCLUSION: The node locations selected by the improved logistics node layout scheme are more reasonable, and e-commerce companies have higher evaluations of the improved logistics node layout scheme.

References

He L Z, Pang Y. Research on the layout and optimization of multi-level fresh fruit and vegetable e-commerce logistics nodes in county and rural areas . Logistics Engineering and Management, 2022, 44(4):84-88.

Pathakota P , Zaid K , Dhara A. Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce.Computer Engineering and Applications 2021, 12(4):36-38.

Zou Y , Si W . Research on Logistics Distribution in E-commerce Environment Based on Particle Swarm Optimization Algorithm. Journal of Physics: Conference Series, 2021, 1881(4):42056-42059.

Beranek L , Remes R . Distribution of Node Characteristics in Evolving Tripartite Network. Entropy, 2020, 22(3):235-263.

Wang X L, Tang J R. Rural E-commerce Logistics Layout and Rural Residents' Consumption--Tracking Based on Rural Taobao . Business Economics Research, 2021, 23(7):77-81.

Wang H Z. Optimization of logistics node layout of international dry ports in Shaanxi Province under the background of "One Belt and One Road" . Logistics Technology, 2018, 37(9):66-69.

Wu M W, Yao Z S. Layout of cold chain logistics nodes in Gansu Province under the "One Belt, One Road" initiative . Integrated Transportation, 2021, 19(4):66-67.

Chen, Dongliang, Pawel Wawrzynski, and Zhihan Lv. "Cyber Security in Smart Cities: A Review of Deep Learning-based Applications and Case Studies." Sustainable Cities and Society (2020): 102655.

Lv H. Analysis on the development mode of intelligent logistics based on the Internet of Things and cloud computing in the era of big data . Communication World, 2017, 12(5):106-110.

Li S S. Application of smart logistics system based on Internet of Things in the era of big data . Electronic Technology and Software Engineering, 2018, 14(6):189-190.

Hopkins J , Hawking P . Big Data Analytics and IoT in logistics: a case study. The International Journal of Logistics Management, 2018, 29(6):575-591.

Raman S , Patwa N , Niranjan I . Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 2018, 42(7):579-596.

Zhang X B, Li W J, Zhou J. Research on logistics distribution route optimization algorithm based on deep learning . Modern Computer: Mid-term, 2017, 14(5):14-20.

Li M, Li W J. Research on swarm intelligence recommendation algorithm for logistics service transactions based on deep learning . Modern Computer, 2021, 27(34):1-11.

Li T J, Huang B, Liu J Y. Application of Convolutional Neural Network Object Detection Algorithm in Logistics Warehouse . Computer Engineering, 2018, 44(6):176-181.

Y Li, Y Zuo, H Song, & Z Lv. (2021). Deep learning in security of internet of things. IEEE Internet of Things Journal, PP(99), 1-1.

Wang X C, Yang Y P, Ji L G. The application of entropy weight coefficient method in the planning and layout of grain logistics nodes in the new era-taking coastal passages as an example . Cereals, Oils and Food Science and Technology, 2022, 30(4): 23-27.

Chen H L, Zhao S, Yang H B. Spatial layout optimization of logistics supply chain nodes around the capital circulation circle . Economic Geography, 2020, 40(7):115-123.

Liu Z, Pan M, Li Y M. Research on node layout of logistics network in Henan Province based on PCA-SNA model . Logistics Technology, 2019, 27(3):28-34.

Hurtado P A , Dorneles C , Frazzon E . Big Data application for E-commerce's Logistics: A research assessment and conceptual model. IFAC-PapersOnLine, 2019, 52(13):838-843.

Dou J. Analysis and Research on Influencing Factors of Gansu Logistics Demand Based on Grey Relevance Model——In the Background of One Belt One Road . Logistics Engineering and Management, 2017, 24(5):47-50.

Lv Y L. Research on regional logistics demand forecasting based on support vector machine . Chinese Market, 2018, 37(2):144-147.

W. Zhou and X. Lei, "Priority-aware resource scheduling for UAV-mounted mobile edge computing networks," IEEE Trans. Vehic. Tech., vol. PP, no. 99, pp. 1–6, 2023.

W. Zhou and F. Zhou, "Profit maximization for cache-enabled vehicular mobile edge computing networks," IEEE Trans. Vehic. Tech., vol. PP, no. 99, pp. 1–6, 2023.

X. Zheng and C. Gao, "Intelligent computing for WPT-MEC aided multi-source data stream," to appear in EURASIP J. Adv. Signal Process., vol. 2023, no. 1, 2023.

Y. Wu and C. Gao, "Intelligent resource allocation scheme for cloud-edge-end framework aided multi-source data stream," to appear in EURASIP J. Adv. Signal Process., vol. 2023, no. 1, 2023.

J. Ling and C. Gao, "DQN based resource allocation for NOMA-MEC aided multi-source data stream," EURASIP J. Adv. Signal Process., vol. 2023, no. 44, pp. 1–15, 2023.

L. He and X. Tang, "Learning-based MIMO detection with dynamic spatial modulation," IEEE Transactions on Cognitive Communications and Networking, vol. PP, no. 99, pp. 1–12, 2023.