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

An Adaptive Importance Sampling-based Approach for Risk Assessment of Supply Chain

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  • Haruto Tanaka,
  • Yuki Nakamura,
  • Aiko Suzuki
Haruto Tanaka
Institute of Logistics Innovation, Shizuoka University of Science and Technology, Shizuoka, 422-8529, Japan
Yuki Nakamura
Center for Advanced Supply Chain Research, Ehime Institute of Technology, Matsuyama, 790-8577, Japan
Aiko Suzuki
Risk Management and Simulation Laboratory, Nara National College of Technology, Nara, 639-1080, Japan
DOI: https://doi.org/10.71070/oaml.v5i1.85

Published 2025-02-08

Keywords

  • Supply Chain Risk Assessment,
  • Computational Inefficiency,
  • Adaptive Importance Sampling,
  • Uncertainty Management,
  • Risk Management Practices

How to Cite

Tanaka, H., Nakamura, Y., & Suzuki, A. (2025). An Adaptive Importance Sampling-based Approach for Risk Assessment of Supply Chain. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.85
Creative Commons License

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

Abstract

Supply chain risk assessment is a critical area of research due to the increasingly complex and interconnected global supply networks. Current studies often face challenges such as computational inefficiency and limited ability to handle uncertainties. This paper addresses these issues by proposing an innovative Adaptive Importance Sampling-based approach. This approach adapts to the changing nature of the supply chain environment and efficiently estimates the risk levels while considering various sources of uncertainty. By integrating advanced sampling techniques with risk assessment models, our work provides a novel methodology to enhance the accuracy and effectiveness of supply chain risk management practices.

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References

  1. 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.
  2. H. Zhang, Y. Shi, X. Yang, and R. Zhou, "A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance," Research in International Business and Finance, vol. 58, p. 101482, 2021/12/01/ 2021, doi: https://doi.org/10.1016/j.ribaf.2021.101482.
  3. A. Karamoozian and D. Wu, "A Hybrid Approach for the Supply Chain Risk Assessment of the Construction Industry During the COVID-19 Pandemic," IEEE Transactions on Engineering Management, vol. 71, pp. 4035-4050, 2024, doi: 10.1109/TEM.2022.3210083.
  4. W. Cui et al., "Phaseevo: Towards unified in-context prompt optimization for large language models," arXiv preprint arXiv:2402.11347, 2024.
  5. J. Zhang, W. Cui, Y. Huang, K. Das, and S. Kumar, "Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models," arXiv preprint arXiv:2410.09629, 2024.
  6. W. Cui et al., "Evaluating and Improving Generation Consistency of Large Language Models via A Divide-Conquer-Reasoning Approach."
  7. M. Li et al., "Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation," Drug development and industrial pharmacy, vol. 42, no. 4, pp. 676-684, 2016.
  8. P. Xiao, M. I. Salleh, B. Zaidan, and Y. Xuelan, "Research on risk assessment of Blockchain-driven Supply Chain finance: A systematic review," Computers & Industrial Engineering, vol. 176, p. 108990, 2023.
  9. C.-L. Zhang, "Risk assessment of supply chain finance with intuitionistic fuzzy information," Journal of Intelligent & Fuzzy Systems, vol. 31, no. 3, pp. 1967-1975, 2016.
  10. L. Pei-Min, "Potential Benefits of Specific Nutrients in the Management of Depression and Anxiety Disorders," Advanced Medical Research, vol. 3, no. 1, pp. 1-10, 12/26 2024, doi: 10.62836/amr.v3i1.283.
  11. P.-M. Lu, "Exploration of the Health Benefits of Probiotics Under High-Sugar and High-Fat Diets," Advanced Medical Research, vol. 2, no. 1, pp. 1-9, 2023.
  12. H. Zhang, L. Song, and G. Bai, "Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator," CMES-Computer Modeling in Engineering & Sciences, vol. 134, no. 3, 2023.
  13. A. Mousavi, R. Monsefi, and V. Elvira, "Hamiltonian adaptive importance sampling," IEEE Signal Processing Letters, vol. 28, pp. 713-717, 2021.
  14. C. Song, Z. Wang, A. Shafieezadeh, and R. Xiao, "BUAK-AIS: Efficient Bayesian updating with active learning Kriging-based adaptive importance sampling," Computer Methods in Applied Mechanics and Engineering, vol. 391, p. 114578, 2022.
  15. S. Tong and G. Stadler, "Large deviation theory-based adaptive importance sampling for rare events in high dimensions," SIAM/ASA Journal on Uncertainty Quantification, vol. 11, no. 3, pp. 788-813, 2023.
  16. Y. Huang, E. Chouzenoux, V. Elvira, and J.-C. Pesquet, "Efficient bayes inference in neural networks through adaptive importance sampling," Journal of the Franklin Institute, vol. 360, no. 16, pp. 12125-12149, 2023.
  17. S. Kanakaraj, M. S. Nair, and S. Kalady, "Adaptive Importance Sampling Unscented Kalman Filter based SAR image super resolution," Computers & Geosciences, vol. 133, p. 104310, 08/01 2019, doi: 10.1016/j.cageo.2019.104310.
  18. X. Xiao, Q. Li, and Z. Wang, "A novel adaptive importance sampling algorithm for Bayesian model updating," Structural Safety, vol. 97, p. 102230, 2022.
  19. A. El Hanchi and D. Stephens, "Adaptive importance sampling for finite-sum optimization and sampling with decreasing step-sizes," Advances in Neural Information Processing Systems, vol. 33, pp. 15702-15713, 2020.
  20. S. He, G. Jiang, H. Lam, and M. C. Fu, "Adaptive importance sampling for efficient stochastic root finding and quantile estimation," Operations Research, vol. 72, no. 6, pp. 2612-2630, 2024.
  21. A. Korba and F. Portier, "Adaptive importance sampling meets mirror descent: a bias-variance tradeoff," in International Conference on Artificial Intelligence and Statistics, 2022: PMLR, pp. 11503-11527.
  22. J. Lei, "Efficient Strategies on Supply Chain Network Optimization for Industrial Carbon Emission Reduction," arXiv preprint arXiv:2404.16863, 2024.
  23. L. Jihu, "Green supply chain management optimization based on chemical industrial clusters," arXiv preprint arXiv:2406.00478, 2024.
  24. J. Lei and A. Nisar, "Examining the influence of green transformation on corporate environmental and financial performance: Evidence from Chemical Industries of China," Journal of Management Science & Engineering Research, vol. 7, no. 2, pp. 17-32, 05/23 2024, doi: 10.30564/jmser.v7i2.6678.