Multi-objective capacity allocation of hospital wards combining revenue and equity
Multi-objective capacity allocation of hospital wards combining revenue and equity
复制标题
收入与权益相结合的医院病房多目标容量分配
DOI：
10.1016/j.omega.2017.11.005
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发表时间：
2017-11
影响因子：
6.9
通讯作者：
Wang Xiuxian
中科院分区：
文献类型：
--
作者：
Zhou Liping;Geng Na;Jiang Zhibin;Wang Xiuxian
由于医院空间有限，建设投资和运营成本大，病房是关键资源。不同类型的患者共享这些服务，其访问时间目标取决于其疾病和付款情况。在中国这样的国家，公立医院在分配这些有限的能力时，必须同时最大限度地提高医院收入和不同类型患者之间的公平性。因此，医院管理者面临着巨大的压力，需要考虑不同类型患者的访问时间目标，并在不减少收入的情况下为他们分配病房。为了解决这个问题，多目标随机规划（MSP）模型的目标是最大化的收入和公平。随机的患者到达和住院时间使得很难在MSP模型中分析描述收入和公平目标。为了科普这个问题，提出了一个数据驱动的离散事件仿真模型，以找到模型目标之间的关系，关于系统性能和决策能力分配和病人入院。然后，基于仿真结果，提出了一种线性化方法，将复杂的多目标随机规划模型转化为多目标整数线性规划（MILP）模型，并提出了一种自适应改进ε-约束算法和一种结合邻域搜索算法的多目标遗传算法来求解MILP问题。以上海某大型公立医院的真实的数据为例，对模型和求解方法进行了数值实验，验证了模型的有效性。
Hospital wards are critical resources because of limited space and large construction investment and operating costs. They are shared among different types of patients with different access time targets determined by their diseases and payments. In countries like China, it is important for public hospitals to simultaneously maximize hospital revenue as well as equity among different types of patients when allocating these limited capacities. Consequently, hospital managers are under high pressure to consider different types of patients’ access time targets and allocate them wards without decreasing revenues. To address this problem, a multi-objective stochastic programming (MSP) model is proposed with the objective to maximize both revenue and equity. Random patient arrivals and lengths of stay make it difficult to analytically describe both revenue and equity objectives in the MSP model. To cope with this problem, a data-driven discrete-event simulation model is proposed to find the relationship between model objectives regarding system performance and decisions regarding capacity allocation and patient admission. Then, based on the simulation results, we propose a linearization approach to transform the complex multi-objective stochastic programming model to a multi-objective integer linear programming (MILP) model, and an adaptive improved ε-constraint algorithm and a multi-objective genetic algorithm combined with neighborhood search algorithm are proposed to solve the MILP problem. Based on the real data collected from a large public hospital in Shanghai, extensive numerical experiments are performed to demonstrate the efficiency of the model and solution approaches.
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DOI：
10.1287/opre.2013.1220
发表时间：
2013-12
期刊：
Oper. Res.
影响因子：
--
作者：
Edieal J. Pinker;Tolga Tezcan
通讯作者：
Edieal J. Pinker;Tolga Tezcan
DOI：
10.2307/1269971
发表时间：
1982
期刊：
--
影响因子：
--
作者：
Fourth Edition;Averill M. Law
通讯作者：
Fourth Edition;Averill M. Law
DOI：
10.1016/j.amc.2009.09.051
发表时间：
2009-12
期刊：
Appl. Math. Comput.
影响因子：
--
作者：
M. Alrefaei;Ali H. Diabat
通讯作者：
M. Alrefaei;Ali H. Diabat
DOI：
--
发表时间：
1983
期刊：
--
影响因子：
--
作者：
V. Chankong;Y. Haimes
通讯作者：
V. Chankong;Y. Haimes
影响因子：
1.6
作者：
Ayvaz, Nur;Huh, Woonghee Tim
通讯作者：
Huh, Woonghee Tim