Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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1.60

CiteScore

Huayu Chu1This email address is being protected from spambots. You need JavaScript enabled to view it., Lichong Cui1, Yuejia Li1, Lei Su1, Yanyang Fu1, and Yuxiang Wang2

1State Grid Hebei Procurement Company, Shijiazhuang, Hebei, 050000, China

2State Grid Baoding Electric Power Supply Company, Baoding, Hebei, 071000, China


 

 

Received: April 24, 2023
Accepted: September 17, 2023
Publication Date: November 16, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202408_27(8).0003  


The relevance of power supply chain resources is a key factor affecting the hit rate of resource integration. Therefore, a 4PL mode based supply chain resource integration method based on associated data is proposed. Firstly, analyze the functions of the power supply chain under the 4PL mode, mainly including intelligent procurement, digital logistics, panoramic quality control, supply chain collaboration, and operational compliance. Secondly, according to the basic characteristics of the 4PL model, which is intensive, valued, standardized, and internationalized, the Apriori association rule algorithm is used for association data mining of power supply chain resources. Finally, based on the results of power supply chain association data mining, the improved ant colony segmentation algorithm is used to divide the power supply chain knowledge base into modules, and the mapping principle is used to complete the integration of supply chain resources. The experimental results show that the proposed integration method realizes effective knowledge base mapping in the process of resource integration, which can improve the utilization of power supply chain resources and reduce energy consumption, with the integration hit rate reaching 99.04%.


Keywords: Associated data; 4PL mode; Power supply chain; Resource integration


  1. [1] K. Matsui, (2021) “Power structure and profitability in a three-echelon supply chain facing stochastic demand" Computers Industrial Engineering 160: 107246. DOI: 10.1016/j.cie.2021.107246.
  2. [2] J. Anderson, (2021) “ISO-NE updates power generators ahead of winter amid supply chain constraints" Platts Energy Trader 9(4): 1–30.
  3. [3] M. Huang, L. Dong, H. Kuang, Z.-Z. Jiang, L. H. Lee, and X. Wang, (2021) “Supply chain network design considering customer psychological behavior-a 4PL perspective" Computers & Industrial Engineering 159: 107484. DOI: 10.1016/j.cie.2021.107484.
  4. [4] H. Wang, M. Huang, W. Ip, and X. Wang, (2021) “Network design for maximizing service satisfaction of suppliers and customers under limited budget for industry innovator fourth-party logistics" Computers & Industrial Engineering 158: 107404. DOI: 10.1016/j.cie.2021.107404.
  5. [5] T. Saikouk, N. Fattam, G. Angappa, and A. Hamdi, (2021) “The interplay between inter-personal and interorganizational relationships in coordinating supply chain activities" The International Journal of Logistics Management 32(3): 898–917. DOI: 10.1108/IJLM11-2020-0443.
  6. [6] W. A. Jauhari, I. N. Pujawan, M. Suef, and I. D. Wangsa, (2021) “Sustainable Electrical Energy Supply Chain System With Hybrid Power Generation: An Inventory Approach" IEEE Access 9: 102207–102224. DOI: 10.1109/ACCESS.2021.3098543.
  7. [7] C. Melo, R. Moita, and S. Sunao, (2021) “Passing through the supply chain: Implications for market power" International Journal of Industrial Organization 79: 102796. DOI: 10.1016/j.ijindorg.2021.102796.
  8. [8] X. Yu, Z. Li, S. Sun, G. Zhang, Z. Diao, and G. Xie, (2021) “Adaptive Virtual Machine Consolidation Method Based on Deep Reinforcement Learning" Journal of Computer Research and Development:
  9. [9] H. Zhang, P. Li, and X. Hu, (2022) “Multi-label Data Stream Ensemble Classification Approach Based on Kernel Extreme Learning Machine" Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing 37: 183–193. DOI: 10.16337/j.1004âAC9037.2022.01.016.
  10. [10] L. Zhao, Z. Chen, L. Ding, and B. Song, (2022) “A weighted ensemble classification method for time series data based on regularized extreme learning machine" Computer Engineering & Science 44: 545–553.
  11. [11] B. Shu, F. Pei, K. Zheng, and M. Yu, (2021) “LIRP optimization of cold chain logistics in satellite warehouse mode of supermarket chains" Journal of Intelligent & Fuzzy Systems 41(4): 4825–4839. DOI: 10.3233/JIFS189968.
  12. [12] G. C. de Bittencourt, R. D. S. Chagas, V. A. Silva, I. G. P. Vianna, R. P. Longhi, P. C. Ribas, and V. J. M. Ferreira Filho, (2021) “A solution framework for the integrated problem of cargo assignment, fleet sizing, and delivery planning in offshore logistics" Computers & Industrial Engineering 161: 107653. DOI: 10.1016/j.cie.2021.107653.
  13. [13] W. Gao, (2021) “Intelligent Prediction Algorithm of Cross-Border E-Commerce Logistics Cost Based on Cloud Computing" Scientific Programming 2021: 1–10. DOI: 10.1155/2021/7038294.
  14. [14] C. Han and Q. Wang, (2021) “Research on commercial logistics inventory forecasting system based on neural network" Neural Computing and Applications 33: 691–706. DOI: 10.1007/s00521-020-05090-4.
  15. [15] H.-B. Wang and Y.-J. Gao, (2021) “Research on parallelization of Apriori algorithm in association rule mining" Procedia Computer Science 183: 641–647. DOI: 10.1016/j.procs.2021.02.109.
  16. [16] J. Guan, G. Shi, and H. Chen, (2022) “Research on Network Vulnerability Mining Based on Apriori Risk Data Analysis" Computer Simulation 39(1): 343–347.
  17. [17] J. Gao, Y. Zhao, L. Li, and S. Deebhijarn, (2021) “Feasibility Analysis Model of Transformation from Real Economy to Virtual Economy Based on Association Rule Algorithm" Mobile Information Systems 2021: 1–9. DOI: 10.1155/2021/3479532.
  18. [18] A. Saxena and V. Rajpoot. “A comparative analysis of association rule mining algorithms”. In: IOP Conference Series: Materials Science and Engineering. 1099. 1. IOP Publishing. 2021, 012032. DOI: 10.1088/1757-899X/1099/1/012032.
  19. [19] B. K. Dewangan, A. Jain, R. N. Shukla, and T. Choudhury, (2022) “An ensemble of bacterial foraging, genetic, ant colony and particle swarm approach EB-GAP: a load balancing approach in cloud computing" Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 15(5): 693–699. DOI: 10.2174/2666255813666201218161955.
  20. [20] M. He, Z. Wei, X. Wu, and Y. Peng, (2021) “An adaptive variable neighborhood search ant colony algorithm for vehicle routing problem with soft time windows" IEEE Access 9: 21258–21266. DOI: 10.1109/ACCESS.2021.3056067.


    



 

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