The traditional particle swarm optimization algorithm exhibits strong randomness during evolutionary process. In the stage of searching for the optimal solution, some particles in the population that deviate from the global optimum can interfere with the convergence direction of the evolutionary process, leading the algorithm to easily fall into a local optimal state. To effectively address this issue, an emergency resource dynamic scheduling method based on improved particle swarm optimization algorithm is proposed. The objective function aims to minimize the total cost of disaster relief scheduling and maximize the fulfillment of actual material demands, and corresponding constraint conditions are established to construct a dynamic scheduling model for emergency resources. Chaos motion theory is used to improve the traditional particle swarm algorithm, obtain the global optimal solution, and the improved particle swarm algorithm is used to solve and calculate the scheduling model to obtain the optimal scheduling plan, achieving precise scheduling of emergency resources in disaster stricken areas. The experimental results show that when using this method for dynamic scheduling of emergency resources, the quantity of material scheduling highly matches the actual demand, and the scheduling path length is relatively short, which verifies its efficiency and reliability in practical applications.

chaos motion theory, dynamic scheduling of emergency resources, particle swarm optimization algorithm, dynamic scheduling model