南科大信管系王松昊助理教授的研究成果被INFORMS Journal on Computing期刊接收

2021-02-23

近日，南方科技大学信息系统与管理工程系王松昊助理教授与其海外合作者的文章A Multi-Level Simulation Optimization Approach for Quantile Functions被INFORMS Journal on Computing期刊接收并即将发表。该文由王松昊与新加坡国立大学Szu Hui Ng 普渡大学William B. Haskell合作撰写。

摘要：Quantile is a popular performance measure for a stochastic system to evaluate its variability and risk. To reduce the risk selecting the actions that minimize the tail quantiles of some loss distributions is typically of interest for decision makers. When the loss distribution is observed via simulations evaluating and optimizing its quantile can be challenging especially when the simulations are expensive as it may cost a large number of simulation runs to obtain accurate quantile estimators. In this work we propose a multi-level metamodel (co-kriging) based algorithm to optimize quantiles more efficiently. Utilizing non-decreasing properties of quantiles we first search on cheaper and informative lower quantiles which are more accurate and easier to optimize. The quantile level iteratively increases to the objective level while the search has a focus on the possible promising regions identified by the previous levels. This enables us to leverage the accurate information from the lower quantiles to find the optimums faster and improve algorithm efficiency.

文章链接：https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2020.1049