基于仿真优化算法的医院关键资源能力分派研究

doi:10.12005/orms.2018.0033

运筹与管理 ›› 2018, Vol. 27 ›› Issue (2): 38-47.DOI: 10.12005/orms.2018.0033

基于仿真优化算法的医院关键资源能力分派研究

陈乐天¹, 耿娜¹, 祝延红², 马磊²

1.上海交通大学工业工程与物流管理系,上海 200240;
2.上海交通大学附属第一人民医院,上海 200240

收稿日期:2015-10-20 出版日期:2018-02-25
作者简介:陈乐天(1990-),男,山东省潍坊市,硕士研究生,研究方向:关键医疗资源能力分派研究;耿娜(1980-),通讯作者,女,山东省淄博市,副教授,研究方向:基于随机优化的生产与服务系统运作管理研究,主要结合医疗服务系统,包括关键医疗资源能力分派研究、手术室的资源优化研究、预约调度等;祝延红(1966-),男,研究员;马磊(1985-),男,硕士研究生。
基金资助:
国家自然科学基金委项目(71471113,71671111,71432006));上海交通大学医工交叉项目(YG2014MS30)

Simulation-based Optimization for Capacity Allocation of Critical Resources in the Hospital

CHEN Le-tian¹, GENG Na¹, ZHU Yan-hong², MA Lei²

1.Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China;
2.Shanghai General Hospital, Shanghai 200240, China

Received:2015-10-20 Online:2018-02-25

摘要/Abstract

摘要： 图像检查设备是医院的瓶颈资源。医院管理者通常追求瓶颈资源的高利用率,导致患者需长时间等待才能进行检查。长时间的等待加剧了患者的焦虑情绪,也会给患者造成病情加重等隐患。考虑患者具有不同的目标等待时间,本文针对医疗关键资源能力分派问题,提出了双层嵌套的阈值策略。非紧急患者预约时,双层嵌套阈值考虑为将来到达的紧急患者预留一定量的能力。如果患者在目标等待时间内预约不成功,则离开医院,并对医院造成患者流失惩罚。目标函数是使患者总流失惩罚最小。本文用目标函数对嵌套阈值的偏导数作为最速梯度法下降方向,基于样本路径对该偏导数进行估计,并通过不断迭代得到最优阈值。数值实验中,与医院应用的传统阈值策略比较,结果显示,本文所提嵌套阈值策略能够有效降低因超过目标等待时间而流失的患者给医院带来的损失。

关键词: 能力分派, 目标等待时间, 嵌套阈值, 仿真优化算法, 最速梯度法

Abstract: Imaging facilities are usually critical resources in the hospital. Managers are under high pressure to pursue the high utilization of these resources, which lead to long waiting time of patients. Long waiting may increase the patients’ anxiety and the potential risk of aggravating patients’ condition. By considering different waiting time target(WTT), this paper proposes a bi-level nested threshold. When non-emergency patients arrive, the nested threshold reserves a certain amount of capacity for the future-arrival emergency patients. When a patient cannot reserve the time slot within his/her WTT, the patient will leave the hospital which incurs a penalty for the hospital. The objective is to minimize the total penalty for losing the patients. By using the partial derivatives of objective function with respective to nested threshold as the descending direction, this paper uses simulation for estimating the partial derivatives and finds the good threshold by iteration. Compared with the traditional nested threshold policy in numerical experiments, the proposed bi-level nested threshold can greatly reduce the penalty for losing patients.

Key words: capacity allocation, waiting time target, nested threshold, simulation-based optimization, the steepest descent algorithm

中图分类号:

C931.1

陈乐天, 耿娜, 祝延红, 马磊. 基于仿真优化算法的医院关键资源能力分派研究[J]. 运筹与管理, 2018, 27(2): 38-47.

CHEN Le-tian, GENG Na, ZHU Yan-hong, MA Lei. Simulation-based Optimization for Capacity Allocation of Critical Resources in the Hospital[J]. Operations Research and Management Science, 2018, 27(2): 38-47.

参考文献

[1] Patrick J, Puterman M L, Queyranne M. Dynamic multipriority patient scheduling for a diagnostic resource[J]. Operations research, 2008, 56(6): 1507-1525.
[2] Ayvaz N, Huh W T. Allocation of hospital capacity to multiple types of patients[J]. Journal of Revenue & Pricing Management, 2010, 9(5): 386-398.
[3] 曹萍萍,唐加福.考虑存在取消预约情形的门诊预约能力分配策略[J].运筹与管理,2014,23(2):250-257.
[4] 阎崇钧,唐加福,姜博文,等.考虑患者选择和公平性的序列预约调度方法[J].系统工程学报,2014,29(1):104-112.
[5] Thomsen M S, Nørrevang O. A model for managing patient booking in a radiotherapy department with differentiated waiting times[J]. Acta oncologica, 2009, 48(2): 251-258.
[6] Schuetz H J, Kolisch R. Approximate dynamic programming for capacity allocation in the service industry[J]. European Journal of Operational Research, 2012, 218(1): 239-250.
[7] Schütz H J, Kolisch R. Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service[J]. Annals of operations research, 2013, 206(1): 401-423.
[8] Zhu L, Geng N, Xie X. MDP-based appointment scheduling for one examination of two types of outpatients[C]//Automation Science and Engineering (CASE), 2013 IEEE International Conference on. IEEE, 2013: 764-769.
[9] Gupta D, Wang L. Revenue management for a primary-care clinic in the presence of patient choice[J]. Operations Research, 2008, 56(3): 576-592.
[10] 朱黎敏,耿娜,谢晓岚.基于MDP的门诊病人图像检查最优预约调度[J].工业工程与管理,2014,19(3):101-108.
[11] 罗利,秦春蓉,罗永.基于马尔可夫决策过程的医疗检查预约优化模型[J].运筹与管理,2014,23(6):12-16.
[12] Truong V A. Optimal advance scheduling[J]. Management Science, 2015.
[13] Van Ryzin G, Vulcano G. Simulation-based optimization of virtual nesting controls for network revenue management[J]. Operations Research, 2008, 56(4): 865-880.
[14] Erdelyi A, Topaloglu H. Computing protection level policies for dynamic capacity allocation problems by using stochastic approximation methods[J]. IIE Transactions, 2009, 41(6): 498-510.
[15] Geng N, Xie X. Optimal dynamic outpatient scheduling for a diagnostic facility with two waiting time targets[J]. IEEE Transactions on Automatic Control, accepted, 2016.
[16] Belobaba P P. OR practice—application of a probabilistic decision model to airline seat inventory control[J]. Operations Research, 1989, 37(2): 183-197.
[17] Belobaba P P. Optimal vs. heuristic methods for nested seat allocation[C]//Presentation at ORSA/TIMS Joint National Meeting, 1992.
[18] Gnsch J, Koch S, Steinhardt C. An EMSR-based approach for revenue management with integrated upgrade decisions[J]. Computers & Operations Research, 2013, 40(10): 2532-2542.

基于仿真优化算法的医院关键资源能力分派研究

Simulation-based Optimization for Capacity Allocation of Critical Resources in the Hospital

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	丁秋雷, 胡祥培, 位娟, 姜洋. 动态需求下蓄冷式多温共配多目标优化模型及算法[J]. 运筹与管理, 2021, 30(12): 13-19.
[2]	刘长石, 王松, 罗亮, 邓胜前. 时变路网条件下联合配送的开放式车辆路径问题[J]. 运筹与管理, 2021, 30(11): 26-33.
[3]	徐鑫亮, 孟蕊, 徐建中. 新媒体环境中品牌粉丝效应形成机理研究[J]. 运筹与管理, 2021, 30(11): 218-225.
[4]	周慧妮, 吴鹏, 王筱纶. 考虑广告投放的不对称竞争企业定价策略研究[J]. 运筹与管理, 2021, 30(11): 232-239.
[5]	杨锋, 陈登辉, 焦传亚. 基于制造商技术创新水平的古诺与斯坦伯格博弈对比分析[J]. 运筹与管理, 2021, 30(10): 18-22.
[6]	王效俐, 李静. 基于并列排名的组合评价方法及其鲁棒性分析[J]. 运筹与管理, 2021, 30(10): 71-79.
[7]	舒萍, 顾奕翀, 王嘉, 白光晗, 罗豪元. 基于马尔科夫过程的储备系统稳态性能分析[J]. 运筹与管理, 2021, 30(9): 43-47.
[8]	苏强, 陈淼. 基于委托代理理论的医疗激励契约研究[J]. 运筹与管理, 2021, 30(7): 203-209.
[9]	姚锋敏, 闫颍洛, 刘珊, 滕春贤. 政府补贴下考虑CSR投入的闭环供应链回收及定价决策[J]. 运筹与管理, 2021, 30(6): 69-76.
[10]	韩世莲, 刘新旺. 物流运输网络多目标最短路问题的模糊满意解[J]. 运筹与管理, 2014, 23(5): 55-61.
[11]	聂斌, 叶文静, 刘迪青, 刘晓卉. 基于B样条和聚类分析的非参数轮廓监控方法研究[J]. 运筹与管理, 2021, 30(5): 79-87.
[12]	姚锋敏, 刘珊, 滕春贤. 两零售商竞争下第三方回收闭环供应链的决策及协调[J]. 运筹与管理, 2021, 30(3): 83-89.
[13]	亓文辉, 祁明亮. 烟羽应急计划区公众撤离模式研究[J]. 运筹与管理, 2020, 29(9): 43-52.
[14]	姚锋敏, 刘珊, 胡宪武, 滕春贤. 公平关切下零售商主导闭环供应链的定价策略[J]. 运筹与管理, 2020, 29(8): 120-127.
[15]	聂斌, 王曦, 胡雪. 非正态变异下的非线性轮廓异常点识别方法研究[J]. 运筹与管理, 2019, 28(1): 101-107.