基于混合智能优化算法的医疗物资库存管理研究

Research on medical supplies inventory management based on hybrid intelligent optimization algorithms

针对传统医疗物资库存管理方法存在的库存过剩或不足等问题,以最小化库存成本和最大化物资利用率为目标,提出了一种基于深度强化学习的混合智能优化算法。该算法融合了深度Q网络的学习能力和进化禁忌搜索算法的全局优化能力,综合考虑全局与局部最优解,实现医疗物资库存管理的精准优化。在实验中,将提出的算法与多种优化方法在真实数据集上进行了对比,结果显示,所提算法在最小化库存成本和最大化物资利用率方面均表现最佳,分别为22212元和0.9507。

A hybrid intelligent optimization algorithm based on deep reinforcement learning is proposed to address the issues of surplus or shortage in traditional medical supplies inventory management,with the goal of minimizing inventory costs and maximizing resource utilization.This algorithm integrates the learning capability of Deep Q⁃Network with the global optimization ability of evolutionary tabu search algorithms,considering both global and local optimal solutions to achieve precise optimization of medical supplies inventory management.In experiments,the proposed algorithm is compared with various optimization methods using real⁃world datasets.The results demonstrate that the proposed algorithm performs the best in minimizing inventory costs and maximizing resource utilization,with values of 22212 CNY and 0.9507.