Integrating Blockchain and Deep Reinforcement Learning for Secure and Efficient Supply Chain Management in Tertiary Institutions
DOI:
https://doi.org/10.54097/ntsgdq10Keywords:
Blockchain, Supply Chain Management, Deep Reinforcement Learning, Zero-Trust SecurityAbstract
The rapid adoption of blockchain technology has led to its increasing use in various domains, including supply chain management. However, the integration of blockchain into supply chain systems poses challenges related to security, efficiency, and regulatory compliance. This paper presents an analysis of a blockchain architecture for file sharing and management in tertiary institutions. The proposed framework combines the Soft Actor-Critic (SAC) deep reinforcement learning algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The paper also discusses the evidentiary significance of blockchain records and the procedural implications of integrating this technology into the U.S. judicial system. The framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles.
References
Liang, Y., Wang, X., Wu, Y. C., Fu, H., & Zhou, M. (2023). A Study on Blockchain Sandwich Attack Strategies Based on Mechanism Design Game Theory. Electronics, 12(21), 4417.
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135.
Hald, K. S., & Kinra, A. (2019). How the blockchain enables and constrains supply chain performance. International Journal of Physical Distribution & Logistics Management, 49(4), 376-397.
Turkanović, M., Hölbl, M., Košič, K., Heričko, M., & Kamišalić, A. (2018). EduCTX: A blockchain-based higher education credit platform. IEEE Access, 6, 5112-5127.
Snyder, L. V., & Shen, Z. J. M. (2019). Fundamentals of supply chain theory. John Wiley & Sons.
Gao, C. Y., Xiao, Z., Qiu, H., & Wang, L. (2020). A review of dynamic inventory control under demand uncertainty. International Journal of Production Research, 58(12), 3742-3762.
Sultana, M., Chai, G., Mamun, K. A., & Alam, K. M. (2021). Deep reinforcement learning in supply chain management: a systematic review and future implications. International Journal of Production Research, 1-24.
Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero trust architecture. NIST Special Publication, 800, 207.
Devi, M. S., Suguna, R., & Joshi, A. S. (2021). Integration of blockchain and IoT in supply chain management system: a review on applications and challenges. Wireless Personal Communications, 1-32.
Kuhn, M. (2020). The evidentiary value of blockchain records. Stanford Journal of Blockchain Law & Policy, 3(1), 1.
Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv preprint arXiv:1801.01290.
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015). Prioritized experience replay. arXiv preprint arXiv:1511.05952.
Wang, X., Wu, Y. C., & Ma, Z. (2024). Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in U.S. judicial processes. Frontiers in Blockchain, 7, 1306058.
Underwood, S. (2016). Blockchain beyond bitcoin. Communications of the ACM, 59(11), 15-17.
Kshetri, N. (2018). Blockchain's roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
Tian, F. (2016, June). An agri-food supply chain traceability system for China based on RFID & blockchain technology. In 2016 13th international conference on service systems and service management (ICSSSM) (pp. 1-6). IEEE.
Nguyen, H., Rezapour, S., Fattahi, M., & Khalilpour, J. (2021). Deep reinforcement learning for inventory control: A comprehensive survey of models and applications. Computers & Industrial Engineering, 158, 107374.
Oroojlooyjadid, A., Snyder, L. V., & Takáč, M. (2020). Applying deep learning to the newsvendor problem. IISE Transactions, 52(4), 444-463.
Gijsbrechts, J., Boute, R. N., Zhang, D. Z., & Van Mieghem, J. A. (2021). Can deep reinforcement learning improve inventory management? Performance and implementation of dual sourcing-mode problems. European Journal of Operational Research, 294(1), 356-371.
Suresh, A., Guttag, J. V., & Eryilmaz, A. (2021). Zero trust architecture for blockchain in supply chain management. arXiv preprint arXiv:2102.07916.
Jayasinghe, D., Pham, Q. V., & Niyato, D. (2021). Blockchain-based decentralized zero-trust security framework for internet of things. IEEE Internet of Things Journal.
Afanasev, M. Y., Krylova, A. A., Shorokhov, S. A., & Zybin, D. G. (2021). Application of zero-trust concept in blockchain-based supply chain management system. In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus) (pp. 1004-1009). IEEE.
Sulkowski, A. J., & Luevano, T. C. (2021). The admissibility of blockchain evidence under the federal rules of evidence. St. Mary's Law Journal, 52(4), 1029-1068.
Ma, Z., Chen, X., Sun, T., Wang, X., Wu, Y. C., & Zhou, M. (2024). Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization. Future Internet, 16(5), 163.
Wang, X., Wu, Y. C., Ji, X., & Fu, H. (2024). Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices. Frontiers in Artificial Intelligence, 7, 1320277.
Federal Rules of Evidence, Rule 401, 403, 901 (2021).
Federal Rules of Evidence, Rule 902(13), 902(14) (2021).
Kuhn, M. (2020). The evidentiary value of blockchain records. Stanford Journal of Blockchain Law & Policy, 3(1), 1.
Natoli, C., Yu, J., Gramoli, V., & Esteves-Verissimo, P. (2019). Deconstructing blockchains: A survey on consensus, membership, and structure. ACM Computing Surveys (CSUR), 52(3), 1-41.
Goodenough, O. R., & Finck, M. (2020). Sustainability, challenges, and opportunities in the emerging field of blockchain law. Sustainability, 12(22), 9691.
Erskine, D. W., & Vora, P. (2020). Cross-border issues in blockchain and the law. Journal of International Business and Law, 20(1), 3.
De Filippi, P., & Wright, A. (2018). Blockchain and the Law: The Rule of Code. Cambridge, MA: Harvard University Press.
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