A Hybrid GA-SA for the Urgent Patients Disturbed Physical Examination Rescheduling Problem Considering Setup Time

模拟退火 计算机科学 中断 调度(生产过程) 数学优化 序列(生物学) 算法 遗传算法 资源限制 实时计算 分布式计算 数学 机器学习 电信 传输(电信) 生物 遗传学
作者
Dandan Zhu,Junqing Sun,Yu Zhao
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:9: 14787-14806 被引量:4
标识
DOI:10.1109/access.2021.3052562
摘要

In the practice of medical services, the occurrence of disturbance events will inevitably interrupt the pre-arranged patient visit sequence and medical resource arrangement, so rescheduling is essential. In this paper, in view of the disturbance event of urgent patients and the setup time of medical equipment that cannot be ignored, we studied the urgent patient disturbance physical examination rescheduling problem that considering setup time. The optimization goal is to minimize the sum of medical equipment's setup time and diagnostic completion time of all patients. In this problem, multiple patients need to be examined in multiple medical equipment, and the setup time of all patients on a medical equipment are sequence-dependent which was rarely considered in the previous medical service scheduling research. One of our contributions is that when constructing the mathematical model for the problem, we first introduced the change on the original patient's visit sequence between rescheduling and initial scheduling should be less than a given upper bound as the constraint to reduce the impact on the original patient. Another contribution is that since the problem addressed is strongly NP-hard, combined the global search performance of the Genetic algorithm (GA) and the local search performance of the Simulated Annealing algorithm (SA), we proposed a hybrid algorithm (HGA-SA) of improved GA and improved SA to solve the problem. Finally, the model and algorithm are verified through extensive simulation experiments, results show that the proposed algorithm has good performance compared with several other existing algorithms.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
rayqiang完成签到,获得积分0
刚刚
隐形曼青的应助被科研通管家采纳,获得10
刚刚
CAPCAP完成签到 ,获得积分10
刚刚
rayq完成签到,获得积分10
刚刚
海派甜心完成签到,获得积分10
1秒前
俭朴觅松完成签到 ,获得积分10
1秒前
2秒前
合法的天空完成签到,获得积分10
3秒前
诸葛朝雪完成签到,获得积分10
3秒前
李健的小迷弟的应助被Horizon采纳,获得10
4秒前
刘一严完成签到 ,获得积分10
5秒前
畅快的胡萝卜完成签到,获得积分10
7秒前
有生之年完成签到,获得积分10
10秒前
纯真的元风完成签到,获得积分10
12秒前
老实的达完成签到 ,获得积分10
13秒前
WHB完成签到,获得积分10
13秒前
15秒前
VENTUS完成签到,获得积分10
17秒前
结实大白完成签到,获得积分10
17秒前
温暖完成签到 ,获得积分10
17秒前
biancaliu完成签到,获得积分10
22秒前
lzr完成签到,获得积分10
22秒前
科研通AI6.4的应助被热心不凡采纳,获得10
23秒前
25秒前
无限的含羞草完成签到,获得积分10
29秒前
yj完成签到,获得积分10
29秒前
31秒前
31秒前
sai完成签到,获得积分10
32秒前
也未可知完成签到 ,获得积分10
35秒前
救我发布了新的文献求助10
36秒前
慕子完成签到 ,获得积分10
38秒前
哈尼完成签到,获得积分10
40秒前
41秒前
Domo完成签到,获得积分10
41秒前
手机吧唧一丢完成签到,获得积分10
43秒前
orixero的应助被高文强采纳,获得10
44秒前
屈奕完成签到,获得积分10
46秒前
鲤鱼安青完成签到 ,获得积分10
47秒前
ADChem_JH完成签到,获得积分10
49秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
中国器官捐献和移植发展报告(2024) 520
Organizational Behavior 510
Arbitrage Theory in Discrete and Continuous Time 500
Production Logging: Theoretical and Interpretive Elements 400
English Longitudinal Study of Ageing: Waves 0-11, 1998-2024 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 有机化学 化学工程 内科学 物理 生物化学 复合材料 催化作用 细胞生物学 人工智能 心理学 无机化学 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 7824159
求助须知:如何正确求助?哪些是违规求助? 9350582
关于积分的说明 20557427
捐赠科研通 7416959
什么是DOI,文献DOI怎么找? 3334391
关于科研通互助平台的介绍 2479717
邀请新用户注册赠送积分活动 2354589