School of Management Engineering, Capital University of Economics and Business, Beijing, China.
Armed Police Command Academy, Tianjin, China.
PLoS One. 2024 May 20;19(5):e0303143. doi: 10.1371/journal.pone.0303143. eCollection 2024.
In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.
针对日益复杂的社会突发事件，本研究通过构建基于联盟区块链技术的应急物流信息可追溯模型（ELITM-CBT），实现了物流信息流和资源配置的优化。利用联盟区块链技术的去中心化、数据不可篡改和透明性等特点，本研究突破了传统应急物流模型的局限性，提高了信息管理的准确性和效率。结合混合遗传模拟退火算法（HGASA），改进后的模型在应急物流场景中表现出显著优势，特别是在总运输时间、总成本和资源分配公平性方面。仿真结果验证了该模型在应急响应及时性和资源分配准确性方面的高效性，为应急物流领域提供了创新性的理论支持和实践方案。未来的研究将探索更高效的共识机制，并结合大数据和人工智能技术，进一步提高应急物流系统的性能和适应性。
