主题：Preservation of Supermodularity in Parametric Optimization: Necessary and Sufficient Conditions on Constraint Structures

主讲人：香港中文大学 龙卓瑜副教授

主持人：大数据研究院 徐亮教授

时间：2021年6月10日（周四）14:00-15:00

举办地点：腾讯会议，会议ID：140 164 583

主办单位：大数据研究院 科研处

主讲人简介：

Daniel Zhuoyu Long is an associate professor in the Department of Systems Engineering & Engineering Management at The Chinese University of Hong Kong. Before joining this department, Long earned his PhD in the Department of Decision Sciences, National University of Singapore in 2013, master degree from Chinese Academy of Science, and bachelor degree from Tsinghua University. His research interests are in inventory control, project management, target-based risk management, and robust optimization.

龙卓瑜，现为香港中文大学系统工程与工程管理学系副教授，博士毕业于新加坡国立大学决策科学系，硕士毕业于中国科学院大学，本科毕业于清华大学。他的研究兴趣是库存控制，项目管理，基于目标的风险管理和稳健优化等。

内容简介：

This paper presents a systematic study of the preservation of supermodularity under parametric optimization, allowing us to derive complementarity among parameters and monotonic structural properties for optimal policies in many operational models. We introduce the new concepts of mostly sublattice and additive mostly sublattice, which generalize the commonly imposed sublattice condition significantly, and use them to establish the necessary and sufficient conditions for the feasible set so that supermodularity can be preserved under various assumptions about the objective functions. Furthermore, we identify some classes of polyhedral sets that satisfy these concepts. Finally, we illustrate the use of our results in assemble-to-order systems.

本讲座系统性介绍在参数优化中保持超模性的运算，借此可在许多运作模型中推导具有良好性质的最优策略。