Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource...

Area of Science:

• Operations Research
• Artificial Intelligence
• Emergency Management

Background:

• Traditional resource scheduling systems struggle with slow decision-making, especially during emergencies.
• Existing methods face significant disadvantages when addressing unexpected events like fires or earthquakes.
• Intelligent scheduling systems heavily rely on effective scheduling algorithms.

Purpose of the Study:

• To develop an advanced multi-objective emergency resource scheduling model.
• To address the limitations of traditional resource scheduling in dynamic and uncertain environments.
• To enhance the response capabilities for critical events through optimized resource allocation.

Main Methods:

• Integration of Vague set theory for handling uncertainty.
• Application of adaptive grid particle swarm optimization for multi-objective problem-solving.
• Construction of a novel multi-objective emergency resource scheduling model.

Main Results:

• The proposed model demonstrates higher resolution accuracy and more reasonable resource allocation.
• The model achieves significantly improved efficiency and speed in dealing with emergency events.
• Handling speed increased by over 3.82 times compared to conventional fuzzy theory models.

Conclusions:

• The Vague set theory and adaptive grid particle swarm optimization-based model offers superior performance for emergency resource scheduling.
• This approach effectively manages uncertainty and optimizes resource allocation during critical incidents.
• The developed model represents a significant advancement over traditional and fuzzy theory-based scheduling methods.