一种结合优化遗传算法的医院资源人才调度方案生成方法

A generation method of hospital resource and talent scheduling scheme combining optimization genetic algorithm

摘要

为了实现更高质量的医院资源和人才调度,研究提出一种结合优化遗传算法的医院资源人才调度方案生成方法。首先,构建包含科室数据、员工信息、技能匹配及任务分析等模块的医院资源人才调度系统,并基于位置信息与任务时间统计建立调度模型;其次,在传统遗传算法框架下,引入时间窗约束及多维硬约束机制,包括技能匹配约束、时间冲突约束、任务顺序约束及任务负载约束,以提高调度方案的可行性与合理性;最后,通过构建对比实验,在全科医院与专科医院两类场景下,对所提方法与人工鱼群算法、多目标进化算法进行对比分析。实验结果表明,在相同规模条件下,所提方法在求解速度、内存占用及数据准确性方面均优于对比方法,其中全科医院8人规模下求解时间为61 ms,专科医院长期运行内存占用约为2.9 GB,数据准确性在300 h后仍保持在99.43%以上。说明研究方法能够高质量高效率地完成医院调度策略生成,提高医院管理的效果。

Abstract

To achieve higher-quality hospital resource and talent scheduling, this study proposes a scheduling scheme generation method based on an optimized genetic algorithm. Firstly, a hospital resource and talent scheduling system is constructed, including modules for department data, staff information, skill matching, and task analysis, and a scheduling model is established based on location information and task time statistics. Secondly, within the framework of the traditional genetic algorithm, time window constraints and multi-dimensional hard constraints are introduced, including skill matching constraints, time conflict constraints, task sequence constraints, and workload constraints, to improve the feasibility and rationality of scheduling schemes. Finally, comparative experiments are conducted in both general hospitals and specialized hospitals, where the proposed method is compared with artificial fish swarm algorithm and multi-objective evolutionary algorithm. The results show that, under the same scale conditions, the proposed method outperforms the comparison methods in terms of solution speed, memory consumption, and data accuracy. Specifically, the solution time is 61 ms for a general hospital with 8 staff members, the memory usage is about 2.9 GB during long-term operation in a specialized hospital, and the data accuracy remains above 99.43% after 300 hours of operation. These results demonstrate that the proposed method can efficiently generate high-quality scheduling strategies and effectively improve hospital management performance.

关键词

医院管理 / 资源调度 / 遗传算法 / 多约束优化 / 时间窗约束 / 智能调度 / 信息化

Key words

hospital management / resource scheduling / genetic algorithm / multi-constraint optimization / time window constraint / intelligent scheduling / informatization