In the process of material supply and demand allocation in the sterilization supply room, conventional particle swarm optimization algorithms are commonly used to solve the allocation scheme, which is prone to falling into local optima, resulting in poor resource utilization efficiency. Therefore, a supply and demand allocation algorithm for material supply chain in the sterilization supply room based on an improved MOPSO (Multi Objective Particle Swarm Optimization) algorithm is proposed. Relying on supply chain algorithms, a rapid scheduling system for materials in the sterilization supply room is constructed. Dynamic medical monitoring data is obtained and is inputted into a spatiotemporal attention network model for learning, to derive the demand for sterilization materials. With meeting material demand as the premise, a supply and demand allocation objective function for the supply chain is set, focusing on minimizing cost and maximizing resource utilization. By utilizing an improved multi-objective particle swarm optimization algorithm combined with a linear differential decrement strategy, the objective function is comprehensively solved to generate the optimal material allocation scheme. Experimental results show that after applying this algorithm, the utilization rate of allocated materials stabilizes at over 85% , achieving full utilization of materials in the sterilization supply room.

disinfection supply room, material allocation, supply chain, improved multi objective particle swarm optimization(MOPSO) algorithm, demand, inertia weight