基于鲁棒优化的云医疗资源配置问题

医疗资源配置优化是云医疗系统高效运行的核心决策,然而,由于这种新型互联网医疗服务系统具有多组织协同、上下转诊以及诊疗时间不确定等特点,上述问题可以描述为需求不确定情形下核心医生服务时间分配优化问题.构建一个以最小化最大医疗服务成本为目标函数的云医疗资源鲁棒配置优化模型,通过引入决策者对患者诊疗时间和转诊概率两种不确定性因素的偏好控制参数,进一步将所建立的min-max模型转化为线性规划模型进行求解.仿真实验结果表明,所提出的模型能够降低不确定性对医疗成本带来的影响,保证云医疗系统运行的鲁棒性.

Robust optimization based medical resource allocation problem in cloud healthcare system

Optimization of medical resource allocation plays an important role to ensure the efficient operation of cloud healthcare systems, which is a new internet healthcare system. However, this optimization problem can be presented as an uncertain optimization problem of allocating core doctor service time due to the multi-organizational collaboration, uncertainty of referral, and randomness of diagnosis and treatment time. Based on the robust optimization theory, this paper presents a medical resource allocation model with the objective function of minimizing the maximal medical serve cost in a cloud healthcare system. In this model, two different uncertain factors, which are the patient diagnosis and treatment time and the patient referral probability, can be considered simultaneously according to the control parameters given by decision-makers. From the experimental analysis, it can be concluded that the presented model can reduce the impact of these uncertain factors of patient demands upon the total medical cost, and ensure the robustness of cloud healthcare system operation.