时间：10月23日（星期日）上午9:00-11:50

地点：腾讯会议

报告人：新加坡国立大学孙秋壮研究员

主讲人简介：

孙秋壮博士现为新加坡国立大学工业系统工程与管理系的研究员。他将于今年加入悉尼大学数学与统计学院担任助理教授。孙博士于2015年在上海交通大学取得工业工程学士学位和计算机科学第二学士学位，于2019年在新加坡国立大学取得工业系统工程博士学位。他的研究兴趣主要在数据驱动决策、工业统计、可靠性工程等。

报告内容简介：

We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed-integer linear program. We derive its certainty equivalent and establish structural properties of the optimal production plan. These properties are then used for real-time condition-based dynamic control of the production rate through reoptimization. By contrast, there is no known structural policy when the problem is cast into a Markov decision process framework. The model is further generalized to the nonlinear production-degradation relation. Based on a real production-degradation data set from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when model misspecification presents.

（承办：管理科学与物流系、科研与学术交流中心）