[1]何雨欣,田云娜*,齐娅惠.基于改进麻雀搜索算法的病患分配调度[J].计算机技术与发展,2024,34(05):163-169.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0055]
　HE Yu-xin,TIAN Yun-na*,QI Ya-hui.Patient Allocation Scheduling Based on Improved Sparrow Algorithm[J].,2024,34(05):163-169.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0055]
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基于改进麻雀搜索算法的病患分配调度()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年05期
页码:
163-169
栏目:
人工智能
出版日期:
2024-05-10

文章信息/Info

Title:
Patient Allocation Scheduling Based on Improved Sparrow Algorithm
文章编号:
1673-629X(2024)05-0163-07
作者:
何雨欣; 田云娜*; 齐娅惠
延安大学 数学与计算机科学学院,陕西 延安 716000
Author(s):
HE Yu-xin; TIAN Yun-na*; QI Ya-hui
School of Mathematics and Computer Science,Yan’an University,Yan’an 716000,China
关键词:
病患分配; 数学模型; 改进麻雀搜索算法; 突发公共卫生事件; 多目标优化
Keywords:
patient allocation; mathematical model; improved sparrow search algorithm; public health emergencies; multi-objective opti-mization
分类号:
TP301.6
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0055
摘要:
在突发性公共卫生事件爆发时,大量的病患群体与有限的医疗资源供给会形成急剧的冲突,影响疫情防控救治效率。 为满足患者就诊需求,提高医疗资源使用效率,文章以最小化平均收治时间、最小化最大收治时间为优化目标建立数学模型,在麻雀搜索算法中引入正余弦搜索策略使产生的个体具有多样性,对最优群体加入 t 分布扰动策略避免算法陷入局部最优。 文章采用麻雀搜索算法、改进麻雀搜索算法、量子粒子群算法分别求解文章提出的数学模型,并将结果进行对比分析。 以 Y 市为例,将 Y 市划分为 9 个人口区域,6 家医院,进行病患分配调度的仿真实验研究。 文章提出的数学模型可以准确描述大流行病期间患者就诊问题,所提算法可快速给出病患救治分配方案,让病患在较短的时间内获得相应救援,同时也可避免具有传染性的疫情进一步扩散。
Abstract:
In the outbreak of sudden public health events,a large number of patient groups and the limited supply of medical resources will form a sharp conflict,affecting the efficiency of epidemic prevention,control and treatment. In order to meet the demand of patients and improve the efficiency of medical resources,we establish a mathematical model with the optimization objectives of minimizing the average admission time and minimizing the maximum admission time,introduce the sine-cosine search strategy in the sparrow search algorithm to make the generated individuals have diversity,and add the t-distribution perturbation strategy to the optimal group to avoid the algorithm falling into the local optimum. We use the sparrow search algorithm,the improved sparrow search algorithm,and the quantum particle swarm algorithm to solve the mathematical model proposed,and compare and analyze the results. Taking city Y as an example,city Y is divided into 9 population areas and 6 hospitals for the simulation experimental study of patient allocation scheduling. The proposed math-ematical model can accurately describe the patient consultation problem during the pandemic,and the proposed algorithm can quickly give the patient treatment allocation plan,so that patients can get the corresponding rescue in a shorter time,and also avoid the further spread of infectious epidemics.

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更新日期/Last Update: 2024-05-10