Abstract
With the continuous deepening of economic upgrading and transformation, the scope of the supply chain of SMEs has gradually expanded. At present, in the process of supply chain resource allocation, the supply chain resource distribution allocation model is often used to study it. But the cost control ability of this model is poor. For this reason, this research designs a supply chain resource distribution allocation model based on deep learning. Select the indicators of the supply chain resource distribution allocation model to determine the principle of resource input, risk compensation, maximum utility and comprehensive optimization. Then construct the objective function of supply chain distribution configuration according to the cost and benefit requirements, and then use deep learning technology to obtain the optimal solution of the objective function scheme. By comparing the model in this paper with the traditional model, we can see that the model in this paper has better cost control ability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jizi, L., Nian, Z., Chunling, L.: Optimization of order-driven production decision making in crowdsourcing supply chain with omnichannel design. Comput. Integr. Manuf. Syst. 25(05), 1248–1258 (2019)
Xuelong, Z., Doudou, W.: An Optimal Design Model of Multi-stage Supply Chain Network With Interval Grey Features. Stat. Dec. 36(01), 167–171 (2020)
Pin, Z., He, X., Fen, L.: The optimal ordering decision under the bargaining power of supply chain partners. J. Ind. Eng. Eng. Manage. 33(04), 130–135 (2019)
Pei, L., Gengjun, G.: P-DEA and Shapley value models based on green supply chain profit distribution. J. Railway Sci. Eng. 15(09), 2448–2454 (2018)
Jiannan, S., Xiaofeng, S.: Operational decision and optimal choice of payment to a supply chain based on wholesale price incentive. Chin. J. Manage. 15(01), 103–110 (2018)
Xiaomei, L., Yangang, F., Jiaxin, S.: Coordination model of telecom supply chain based on network externality. J. Qiqihar Univ. (Nat. Sci. Edn.) 36(01), 45–50+67 (2020)
Nannan, Y., Bogen, D.: Bi-level programming model of port supply chain coordination under revenue sharing contract. J. Shanghai Marit. Univ. 40(03), 80–86 (2019)
Shuai, L., Zhaojun, L., Yudong, Z., et al.: Introduction of key problems in long-distance learning and training. Mob. Netw. Appl. 24(1), 1–4 (2019)
Shuai, L., Mengye, L., Hanshuang, L., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. (10), 120 (2019)
Weina, F., Shuai, L., Gautam, S.: Optimization of big data scheduling in social networks. Entropy 21(9), 902–918 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Guan, Y., Yu, L. (2021). Design of Supply Chain Resource Distribution Allocation Model Based on Deep Learning. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-82562-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82561-4
Online ISBN: 978-3-030-82562-1
eBook Packages: Computer ScienceComputer Science (R0)