摘要
为了解决突发事件下应急手术调度问题,将应急手术调度问题看作混合流水车间调度问题,并考虑了医护人员长时间工作而带来的疲劳效应和截断学习效应,构建了术前、术中、术后三阶段手术调度模型。利用改进的灰狼优化算法对三阶段应急手术调度模型进行求解,并通过仿真实验测试模型和算法的有效性。算例分析中,将改进的灰狼优化算法和传统的灰狼优化算法的算例结果进行对比,结果表明改进的灰狼优化算法更有效,能得到更好的调度结果。
From the perspective of the surgical scheduling problem in emergency,this paper considers the fatigue effect and the truncated learning effect caused by medical staffs’long-time work,regards the surgical scheduling problem as the hybrid flow-shop scheduling problem and proposes a three-stage surgical scheduling model which includes preoperative,intraoperative and postoperative.The improved grey wolf optimization is used to solve the emergency surgical scheduling model,and the effectiveness of the model and the algorithm are tested through simulation experiments.The results show that the improved grey wolf optimization algorithm can solve the emergency surgical scheduling model more effectively and get better scheduling results.
作者
成舒凡
叶春明
CHENG Shufan;YE Chunming(School of Business,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《计算机工程与应用》
CSCD
北大核心
2022年第13期295-302,共8页
Computer Engineering and Applications
基金
上海市科委软科学重点项目(20692104300)
国家自然科学基金(71840003)
上海理工大学科技发展基金(2018KJFZ043)。
关键词
手术调度
疲劳效应
学习效应
灰狼优化算法
surgical scheduling
fatigue effect
learning effect
grey wolf optimization algorithm
作者简介
成舒凡(1997—),男,硕士研究生,研究方向为智能算法、医疗调度,E-mail:127414117@qq.com;叶春明(1964—),男,博士,教授,研究方向为智能算法、生产调度。
