| 基于仿真优化算法的医院关键资源能力分派研究 |
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| 引用本文： | 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. DOI: 10.12005/orms.2018.0033 |
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| 作者姓名： | 陈乐天 耿娜 祝延红 马磊 |
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| 作者单位： | 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 |
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| 基金项目： | 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) |
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| 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。
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| 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 |
| 收稿时间： | 2015-10-20 |
Simulation-based Optimization for Capacity Allocation of Critical Resources in the Hospital |
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| 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. DOI: 10.12005/orms.2018.0033 |
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| Authors: | CHEN Le-tian GENG Na ZHU Yan-hong MA Lei |
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| Affiliation: | 1.Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China; 2.Shanghai General Hospital, Shanghai 200240, China |
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| 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. |
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| Keywords: | capacity allocation waiting time target nested threshold simulation-based optimization the steepest descent algorithm |
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