作为互联网环境下一种新型的医疗服务模式，远程医疗克服了时间、地域上的障碍，能够有效实现优质医疗资源向基层医院的辐射，缓解我国“看病难、看病贵”的难题。本项目基于合作单位国家远程医疗中心的数据，采用数据分析方法挖掘远程医疗患者、远程专家医生的行为特征和需求预测模式，并针对远程医疗服务过程中的不确定因素及其独特性，以最大化远程患者的总效用、最小化远程医疗服务系统的总时间（患者等待时间、专家医生加班时间）为目标，利用机会约束、随机规划、分布式鲁棒优化和动态规划等方法，从远程患者预约调度和远程专家医生排程两个方面，构建远程医疗资源调度优化模型，并设计启发式算法求解最优调度规则。研究成果将丰富医疗服务运营管理理论和体系，可为开发远程专家医生的在线调度应用程序、制定相关政策提供理论参考和实践指导。

As a new medical service mode under the Internet environment, telemedicine overcomes the time and geographical barriers, and can effectively realize the open sharing of high-quality medical resources in a wider range and among more patients. To a certain extent, it can alleviate the problem of "difficult and expensive access to medical services" in China. In view of the uncertain factors in the process of remote medical services, this project studies the behavioral characteristics and demand forecasting by combining data analysis and optimization theory. Further, in order to achieve the goal of maximizing the total utility of remote patients and minimizing the total time of telemedicine service system (patient waiting time, expert doctor's overtime), this project studies appointment scheduling of remote patients and resource scheduling of remote experts by building optimization models and designing solution algorithms, the models and algorithms are based on machine learning techniques, chance constrained stochastic programming, distributed robust optimization and dynamic programming. The research results of this project can not only enrich the theory and system of medical service operation management, but also provide theoretical references for the development of online scheduling applications and the formulation of policies by healthcare decision maker.