Blockchain integrated optimized supply chain security using horse herd algorithm
• Key Features of the Proposed Method: • Combines blockchain’s transparency with HHA’s optimization for robust security. • Enables real-time monitoring and adaptive threat response. • Enhances supply chain resilience, authenticity, and efficiency. • This integrated approach offers a scalable, adaptable solution for securing complex supply chains, with broad applicability across industries that demand high levels of operational trust and integrity. This study presents a novel framework integrating blockchain technology with the Horse Herd Algorithm (HHA) to optimize supply chain security across multiple stages. The decentralized and immutable nature of blockchain ensures transparency, traceability, and security, while HHA optimizes security protocols to reduce vulnerabilities and enhance efficiency. The integration enables real-time monitoring, threat detection, and adaptive response, ensuring the integrity of goods and assets throughout the sup-ply chain. The proposed framework leverages blockchain's distributed ledger technology to record transactions and track product movement, ensuring authenticity and ac-countability. HHA's optimization capabilities enhance the framework's ability to detect and respond to potential threats, minimizing the risk of disruptions and losses. Simulation results demonstrate the effectiveness of the blockchain-HHA synergy in mitigating supply chain risks, improving resilience, and ensuring product authenticity. This research provides a robust solution for securing complex supply chains, offering significant implications for industries reliant on secure and efficient supply chain operations. The framework's scalability and adaptability make it an attractive solution for various supply chain applications.
Authors
- W Vinil Dani
- P. M. Joe Prathap
- L. Sherin Beevi (ORCID: https://orcid.org/0000-0002-1180-7550)
- S Swetha
Institutions
- CEPT University (IN)
- Aims Community College (US)
- R.M.D. Engineering College
Publication Details
- Journal
- Results in Engineering
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1016/j.rineng.2025.106950
- Citations
- 1
- Primary Topic
- Internet of Things and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.25