中文摘要：

针对妇产科孕妇产检多重入、周期长的特点,建立以最小化违背孕妇偏好的惩罚成本和医生的加班成本为目标的孕妇产检时间指派优化随机规划模型。利用蒙特卡洛仿真的方法模拟了多个场景下未来孕妇到达的不确定性及其偏好的不确定性,将随机规划模型转化成了线性规划模型。为了减少计算时间,基于短视策略提出了只考虑当周到达孕妇的线性规划模型和贪婪算法。数值实验表明,基于多场景的随机规模模型得到的结果最好（总成本最低）,但计算时间长;而只考虑当周到达的线性规划模型方法和贪婪算法计算时间较短,但求解精度稍差。参数的敏感度分析发现,到达率越高、孕妇偏好分布越集中,总成本就会越高。

英文摘要：

Pregnant women in obstetrical department have the feature of long-term and multi- reentrant examination. A stochastic programming model was put forward with the aim of minimizing the penalty cost of preference violation and doctors＇ overtime cost for the examinations. The Monte Carlo method was adopted to handle the uncertainty of pregnant women＇s future arrival and preferences for the examination time. A linear programming model was proposed based on the number of arrived pregnant women in the current week and a greedy algorithm using Myopic Strategy to reduce the computational time. Numerical experiments show that the stochastic programming approach is better than the other two approaches with longer computing time. Sensitivity analysis shows that larger arrival rate and larger dispersion of preference distribution lead to higher penalty cost.