基于改进MOPSO算法的消毒供应室物资供应链供需调配算法

Supply and Demand Allocation Algorithm for Material Supply Chain in Disinfection Supply Room Based on Improved MOPSO Algorithm

摘要

针对消毒供应室物资供需调配过程中, 通常依靠常规粒子群算法求解调配方案, 其容易陷入局部最优, 导致调度资源利用效果差的问题。因此, 提出基于改进MOPSO(Multi Objective Particle Swarm Optimization)算法的消毒供应室物资供应链供需调配算法。依托供应链算法, 构造消毒供应室物资快速调度体系。获取动态医疗监测数据, 将其输入时空注意力网络模型中进行学习, 得出消毒物资需求量。以满足物资需求为前提, 设置以成本最小化、资源利用率最大化为核心的供应链供需调配目标函数。利用结合线性微分递减策略的改进多目标粒子群优化算法, 对目标函数进行全面求解, 从而生成最佳物资调配方案。实验结果表明, 应用该算法后, 调配物资利用率超过85%, 实现了消毒供应室物资的充分利用。

Abstract

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.

关键词

消毒供应室 / 物资调配 / 供应链 / 改进MOPSO算法 / 需求量 / 惯性权重

Key words

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

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基金资助

河北省自然科学基金资助项目(U20A20179)