Vol. 5 No. 1 (2025): Issue 5
Articles

Modelling of Network Supply Chain through Dynamic Bayesian Networks

PDF
  • Alba Fernández,
  • Osman Kaya,
  • Marcos López,
  • Elena Ruiz
Alba Fernández
Institute of Emerging Technologies, University of Almería, Almería, 04120, Spain
Bio
Marcos López
Centre for Applied Data Sciences, Universidad de Jaén, Jaén, 23071, Spain
Elena Ruiz
Department of Complex Systems, Universitat de Lleida, Lleida, 25003, Spain
DOI: https://doi.org/10.71070/oaml.v5i1.124

Published 2025-04-29

Keywords

  • Supply Chains,
  • Dynamic Modeling,
  • Bayesian Networks,
  • Graphical Techniques,
  • Decision-Making

How to Cite

Fernández, A., Kaya, O., López, M., & Ruiz, E. (2025). Modelling of Network Supply Chain through Dynamic Bayesian Networks. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.124
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

In the era of globalized trade, the management of network supply chains is crucial for the efficiency and competitiveness of business operations. However, existing research has identified significant gaps in modeling and analyzing the dynamic behavior of network supply chains, leading to suboptimal decision-making processes. This paper addresses this challenge by proposing a novel approach utilizing Dynamic Bayesian Networks to model the complex interactions and uncertainties within network supply chains. By integrating probabilistic graphical modeling techniques with dynamic system analysis, our research aims to provide a comprehensive framework for optimizing network supply chain operations. Through a series of case studies and simulations, we demonstrate the effectiveness and potential impact of our proposed methodology in enhancing the resilience and adaptability of network supply chains in the face of uncertainties and disruptions.

PDF

References

  1. S. Shukla and P. Singh, "Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency," International Journal of Innovative Science and Research Technology, 2024.
  2. A. L. Cheng et al., "Electric vehicle battery chemistry affects supply chain disruption vulnerabilities," Nature Communications, 2024.
  3. S. Abbasi et al., "Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry," Environmental Modelling and Assessment, 2022.
  4. Q. Fu et al., "Sustainable Supply Chain and Business Performance: The Impact of Strategy, Network Design, Information Systems, and Organizational Structure," 2022.
  5. M. Akbari-Kasgari et al., "Designing a resilient and sustainable closed-loop supply chain network in copper industry," Clean Technologies and Environmental Policy, 2022.
  6. S. Khalilpourazari and H. Hashemi Doulabi, "A flexible robust model for blood supply chain network design problem," Annals of Operations Research, 2022.
  7. L. Chen and S. Su, "Optimization Of The Trust Propagation On Supply Chain Network Based On Blockchain Plus," Journal of Intelligent Management Decision, 2022.
  8. A. Dolgui et al., "Reconfigurable supply chain: the X-network," International Journal of Production Research, 2020.
  9. M. Chowdhury et al., "Supply chain relational capital for sustainability through governance: the moderating effect of network complexity," Supply Chain Management: An International Journal, 2022.
  10. K. P. Murphy and S. J. Russell, "Dynamic Bayesian networks: representation, inference and learning," 2002.
  11. A. Doucet et al., "Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks," Conference on Uncertainty in Artificial Intelligence, pp. 499-515, 2000.
  12. H. O. Caetano et al., "Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation," Reliability Engineering & System Safety, vol. 241, pp. 109691, 2023.
  13. Y. Tang and C. Li, ‘Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises,’ Journal of Computational Methods in Engineering Applications, pp. 1–17, 2023.
  14. Q. Zhu, ‘Autonomous Cloud Resource Management through DBSCAN-based unsupervised learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Jun. 2025, doi: 10.71070/oaml.v5i1.112.
  15. S. Dan and Q. Zhu, ‘Enhancement of data centric security through predictive ridge regression’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, May 2025, doi: 10.71070/oaml.v5i1.113.
  16. S. Dan and Q. Zhu, ‘Highly efficient cloud computing via Adaptive Hierarchical Federated Learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Apr. 2025, doi: 10.71070/oaml.v5i1.114.
  17. Q. Zhu and S. Dan, ‘Data Security Identification Based on Full-Dimensional Dynamic Convolution and Multi-Modal CLIP’, Journal of Information, Technology and Policy, 2023.
  18. Q. Zhu, ‘An innovative approach for distributed cloud computing through dynamic Bayesian networks’, Journal of Computational Methods in Engineering Applications, 2024.
  19. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  20. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  21. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  22. Y. Shu, Z. Zhu, S. Kanchanakungwankul, and D. G. Truhlar, ‘Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods’, J. Phys. Chem. A, vol. 128, no. 31, pp. 6412–6422, Aug. 2024, doi: 10.1021/acs.jpca.4c03137.
  23. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  24. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  25. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  26. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  27. Z. Zhao, P. Ren, and Q. Yang, ‘Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America’, Apr. 17, 2024, arXiv: arXiv:2404.11029. doi: 10.48550/arXiv.2404.11029.
  28. Z. Zhao, P. Ren, and M. Tang, ‘Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China’, Journal of Linguistics and Education Research, vol. 5, no. 2, pp. 15–31, 2022.
  29. M. Tang, P. Ren, and Z. Zhao, ‘Bridging the gap: The role of educational technology in promoting educational equity’, The Educational Review, USA, vol. 8, no. 8, pp. 1077–1086, 2024.
  30. P. Ren, Z. Zhao, and Q. Yang, ‘Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China’, Apr. 17, 2024, arXiv: arXiv:2404.11034. doi: 10.48550/arXiv.2404.11034.
  31. P. Ren and Z. Zhao, ‘Parental Recognition of Double Reduction Policy, Family Economic Status And Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource’, Economics & Management Information, pp. 1–12, 2024.
  32. Z. Zhao, P. Ren, and M. Tang, ‘How Social Media as a Digital Marketing Strategy Influences Chinese Students’ Decision to Study Abroad in the United States: A Model Analysis Approach’, Journal of Linguistics and Education Research, vol. 6, no. 1, pp. 12–23, 2024.
  33. Z. Zhao and P. Ren, ‘Identifications of Active Explorers and Passive Learners Among Students: Gaussian Mixture Model-Based Approach’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  34. Z. Zhao and P. Ren, ‘Prediction of Student Answer Accuracy based on Logistic Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Feb. 2025.
  35. Z. Zhao and P. Ren, ‘Prediction of Student Disciplinary Behavior through Efficient Ridge Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Mar. 2025.
  36. Z. Zhao and P. Ren, ‘Random Forest-Based Early Warning System for Student Dropout Using Behavioral Data’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Apr. 2025.
  37. P. Ren and Z. Zhao, ‘Recognition and Detection of Student Emotional States through Bayesian Inference’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  38. P. Ren and Z. Zhao, ‘Support Vector Regression-based Estimate of Student Absenteeism Rate’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Jun. 2025.
  39. G. Zhang and T. Zhou, ‘Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model’, IAET, pp. 1–13, Sep. 2024, doi: 10.62836/iaet.v3i1.232.
  40. G. Zhang, W. Huang, and T. Zhou, ‘Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation’, Electrical Science & Engineering, vol. 6, no. 1, Art. no. 1, Oct. 2024, doi: 10.30564/ese.v6i1.7532.
  41. T. Zhou, G. Zhang, and Y. Cai, ‘Unsupervised Autoencoders Combined with Multi-Model Machine Learning Fusion for Improving the Applicability of Aircraft Sensor and Engine Performance Prediction’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Feb. 2025, doi: 10.71070/oaml.v5i1.83.
  42. C. Li and Y. Tang, ‘Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region’, Economics & Management Information, pp. 1–13, 2024.
  43. C. Li and Y. Tang, ‘The Factors of Brand Reputation in Chinese Luxury Fashion Brands’, Journal of Integrated Social Sciences and Humanities, pp. 1–14, 2023.
  44. C. Y. Tang and C. Li, ‘Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises’, OAJRC Social Science, vol. 4, no. 3, pp. 63–77, 2023.
  45. W. Huang, T. Zhou, J. Ma, and X. Chen, ‘An ensemble model based on fusion of multiple machine learning algorithms for remaining useful life prediction of lithium battery in electric vehicles’, Innovations in Applied Engineering and Technology, pp. 1–12, 2025.
  46. W. Huang and J. Ma, ‘Predictive Energy Management Strategy for Hybrid Electric Vehicles Based on Soft Actor-Critic’, Energy & System, vol. 5, no. 1, 2025.
  47. J. Ma, K. Xu, Y. Qiao, and Z. Zhang, ‘An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms’, Journal of Computational Methods in Engineering Applications, pp. 1–12, 2022.
  48. W. Huang, Y. Cai, and G. Zhang, ‘Battery Degradation Analysis through Sparse Ridge Regression’, Energy & System, vol. 4, no. 1, Art. no. 1, Dec. 2024, doi: 10.71070/es.v4i1.65.
  49. Z. Zhang, ‘RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations’, Optimizations in Applied Machine Learning, vol. 4, no. 1, 2024.
  50. Z. Zhang, K. Xu, Y. Qiao, and A. Wilson, ‘Sparse Attention Combined with RAG Technology for Financial Data Analysis’, Journal of Computer Science Research, vol. 7, no. 2, Art. no. 2, Mar. 2025, doi: 10.30564/jcsr.v7i2.8933.
  51. P.-M. Lu and Z. Zhang, ‘The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering’, Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025.
  52. Y. Qiao, K. Xu, Z. Zhang, and A. Wilson, ‘TrAdaBoostR2-based Domain Adaptation for Generalizable Revenue Prediction in Online Advertising Across Various Data Distributions’, Advances in Computer and Communication, vol. 6, no. 2, 2025.
  53. K. Xu, Y. Gan, and A. Wilson, ‘Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances’, Innovations in Applied Engineering and Technology, pp. 1–12, 2024.
  54. A. Wilson and J. Ma, ‘MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection’, Optimizations in Applied Machine Learning, vol. 5, no. 1, 2025.