Study on Emergency Surgical Scheduling with Fatigue Effect and Learning Effect

doi:10.3778/j.issn.1002-8331.2011-0337

Abstract

Abstract: 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.

Key words: surgical scheduling, fatigue effect, learning effect, grey wolf optimization algorithm

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。

关键词: 手术调度, 疲劳效应, 学习效应, 灰狼优化算法

CHENG Shufan, YE Chunming. Study on Emergency Surgical Scheduling with Fatigue Effect and Learning Effect[J]. Computer Engineering and Applications, 2022, 58(13): 295-302.

成舒凡, 叶春明. 嵌入疲劳效应和学习效应的应急手术调度研究[J]. 计算机工程与应用, 2022, 58(13): 295-302.

References

[1] 王付宇，叶春明，李卫宁，等.考虑学习效应的地震伤员手术调度模型及算法[J].工业工程与管理，2018，23（4）：1-7.
WANG F Y，YE C M，LI W N，et al.Operation scheduling model and algorithm design of earthquake casualty considering learning effect[J].Industrial Engineering and Management，2018，23（4）：1-7.
[2] 李惠，蒋大奎.基于单亲遗传禁忌搜索算法的手术排程问题研究[J].计算机应用研究，2013，30（3）：699-702.
LI H，JIANG D K.Research on operating room scheduling problem based on partheno-genetic tabu search algorithm[J].Application Research of Computers，2013，30（3）：699-702.
[3] 白雪，罗利，杨晨曦，等.生产调度理论在医院手术室调度优化中的应用研究框架[J].华西医学，2015，30（10）：1990-1995.
BAI X，LUO L，YANG，C X，et al.Application research framework of production scheduling theory in hospital operating room scheduling optimization[J].West China Medical Journal，2015，30（10）：1990-1995.
[4] 朱悦.考虑服务时间约束的手术排程研究[D].南京：东南大学，2016.
ZHU Y.Surgical scheduling under service time constraints[D].Nanjing：Southeast University，2016.
[5] ZHONG L，LUO S，WU L，et al.A two-stage approach for surgery scheduling[J].Journal of Combinatorial Optimization，2014，27（3）：545-556.
[6] BISKUP D.Single-machine scheduling with learning consideration[J].European Journal of Operational Research，1999，115：173-178.
[7] LEE W C.A note on deteriorating jobs and learning in single-machine scheduling problems[J].International Journal of Business and Economics，2004，3（1）：83-89.
[8] WANG X R，WANG J B，JIN J，et al.Single machine scheduling with truncated job-dependent learning effect[J].Optimization Letters，2014，8（2）：669-677.
[9] NIU Y P，WANG J，YIN N.Scheduling problems with effects of deterioration and truncated job-dependent learning[J].Journal of Applied Mathematics and Computing，2015，47（1/2）：315-325.
[10] 姚远远，叶春明.求解作业车间调度问题的改进混合灰狼优化算法[J].计算机应用研究，2018，35（5）：1310-1314.
YAO Y Y，YE C M.Solving job-shop scheduling problem using improved hybrid grey wolf optimizer[J].Application Research of Computers，2018，35（5）：1310-1314.
[11] 龙文，伍铁斌，唐明珠，等.基于透镜成像学习策略的灰狼优化算法[J].自动化学报，2020，46（10）：2148-2164.
LONG W，WU T B，TANG M Z，et al.Grey wolf optimizer algorithm based on lens imaging learning strategy[J].Acta Automatica Sinica，2020，46（10）：2148-2164.
[12] 姜天华.基于灰狼优化算法的低碳车间调度问题[J].计算机集成制造系统，2018，24（10）：2428-2435.
JIANG T H.Low-carbon workshop scheduling problem based on grey wolf optimization[J].Computer Integrated Manufacturing Systems，2018，24（10）：2428-2435.
[13] LU C，GAO L，PAN Q K，et al.A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution[J].Applied Soft Computing，2019，75：728-749.
[14] 殷姣.基于多资源约束柔性工件车间调度的手术排程优化研究[D].宁波：宁波大学，2013.
YIN J.Surgery scheduling optimization based on flexible job-shop scheduling under multi-resource constraints[D].Ningbo：Ningbo University，2013.
[15] 杨枫.考虑疲劳效应的伤员应急手术调度模型及算法[J].工业工程，2020，23（2）：91-99.
YANG F.Emergency surgical scheduling model and algorithm design of casualty considering fatigue effect in the urban emergency[J].Industrial Engineering Journal，2020，23（2）：91-99.
[16] 刘春来，王建军.具有退化工件和老化效应的单机可拒绝排序问题[J].运筹与管理，2017，26（6）：95-101.
LIU C L，WANG J J.Single-machine scheduling with deteriorating jobs，aging effect and rejection[J].Operations Research and Management Science，2017，26（6）：95-101.
[17] 王雪茹，白雪莲，王吉波，等.基于截断学习效应的流水作业排序问题研究[J].重庆师范大学学报（自然科学版），2017，34（5）：12-17.
WANG X R，BAI X L，WANG J B，et al.Research on flow shop scheduling problem based on truncation learning effect[J].Journal of Chongqing Normal University（Natural Science），2017，34（5）：12-17.
[18] ARNAOUT J P M，KULBASHIAN S.Maximizing the utilization of operating rooms with stochastic times using simulation[C]//Proceedings of the 40th Conference on Winter Simulation，December 7-10，2008：1617-1623.
[19] SAREMI S，MIRJALILI S Z，MIRJALILI S M.Evolutionary population dynamics and grey wolf optimizer[J].Neural Computing and Applications，2015，26（5）：1257-1263.
[20] 龙文，赵东泉，徐松金.求解约束优化问题的改进灰狼优化算法[J].计算机应用，2015，35（9）：2590-2595.
LONG W，ZHAO D Q，XU S J.Improved grey wolf optimization algorithm for constrained optimization problem[J].Journal of Computer Applications，2015，35（9）：2590-2595.
[21] 姚远远，叶春明，杨枫.双目标可重入混合流水车间调度问题的离散灰狼优化算法[J].运筹与管理，2019，28（8）：190-199.
YAO Y Y，YE C M，YANG F.Solving bi-objective reentrant hybrid flow shop scheduling problems by a hybrid discrete grey wolf optimizer[J].Operations Research and Management Science，2019，28（8）：190-199.
[22] SONG H M，SULAIMAN M H，MOHAMED M R.An application of grey wolf optimizer for solving combined economic emission dispatch problems[J].International Review on Modelling and Simulation，2014，7（5）：838-844
[23] BILGILI F，DIKMEN G，ALI B，et al.Acute traumatic posterior fracture dislocation of the elbow in pediatric patients：Impact of surgery time and associated fractures on outcome[J].Journal of Pediatric Orthopedics（Part B），2016，25（5）：434.