Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain

PLoS One. 2024 May 20;19(5):e0303143. doi: 10.1371/journal.pone.0303143. eCollection 2024.

Abstract

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.

MeSH terms

  • Algorithms*
  • Blockchain*
  • Emergencies
  • Humans
  • Models, Theoretical
  • Resource Allocation*

Grants and funding

The Beijing Social Science Fund Project (Grant No. 20GLB028), with Mr. Chuansheng Wang as the funder, is primarily responsible for the methodology and resources sections of this research; The Beijing Municipal Universities Basic Research Funds in Capital University of Economics and Business (Grant No. XRZ2022029) and the Ministry of Education, Humanities and Social Sciences project (Grant No. 23YJCZH182), with Mr. Fulei Shi as the funder, focuses mainly on the methodology part of the project.