A planning method based on artificial bee colony optimization algorithm ABC(Artificial Bee Colony) is proposed to optimize the cross regional allocation path of emergency disinfection materials after natural disasters. Using the entropy weight method to comprehensively evaluate multidimensional indicators such as disaster area, population density, and medical resource gaps in designated hospitals, the urgency of demand for each disaster site and related hospital is quantified. Based on the quantification results, the emergency area is divided into four levels: red, orange, yellow, and blue, in order to achieve priority scheduling in different zones. A fitness function for the artificial bee colony optimization algorithm is designed, which includes total delivery time and unmet urgent needs. Artificial bee colony optimization algorithm is used to solve and find the optimal planning scheme for the allocation path. The experimental results show that under two typical operating conditions, the maximum fitness functions reach 0. 97 and 0. 95, respectively, which is 3. 19% to 13. 10% higher than the artificial fish swarm algorithm and genetic algorithm. It significantly improves the timeliness and emergency priority of material distribution for key institutions such as hospitals.

emergency supply distribution, demand urgency, regional zoning, fitness function design, artificial bee colony (ABC)algorithm, path optimization