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

Area of Science:

• Logistics Management
• Information Systems
• Supply Chain Optimization

Background:

• Increasingly complex social emergencies necessitate advanced logistics solutions.
• Traditional emergency logistics models face limitations in information management and resource allocation.
• The need for transparent, immutable, and efficient data handling in crisis response is critical.

Purpose of the Study:

• To optimize emergency logistics information flow and resource allocation.
• To develop a novel Emergency Logistics Information Traceability Model (ELITM-CBT) utilizing alliance blockchain technology.
• To enhance the accuracy and efficiency of information management in emergency scenarios.

Main Methods:

• Construction of the Emergency Logistics Information Traceability Model (ELITM-CBT) based on alliance blockchain.
• Leveraging the decentralized, immutable, and transparent characteristics of blockchain technology.
• Integration with the hybrid genetic simulated Annealing algorithm (HGASA) for optimization.

Main Results:

• The ELITM-CBT model demonstrates significant improvements in emergency logistics.
• Key advantages observed in reduced total transportation time and total cost.
• Enhanced fairness in resource allocation and improved accuracy of information management.
• Simulation results validate high efficiency in emergency response timeliness and resource allocation accuracy.

Conclusions:

• The ELITM-CBT model offers innovative theoretical support and a practical scheme for emergency logistics.
• The study highlights the effectiveness of blockchain technology in overcoming traditional logistics limitations.
• Future research directions include exploring advanced consensus mechanisms and integrating big data and AI.