Personalized optimization and its implementation in computer experiments. (3rd May 2020)

• Record Type: Journal Article
• Authors: Xiong, Shifeng
• Is Part Of: IISE transactions. Volume 52:Number 5(2020)
• Page Start: 528
• Page End: 536
• Publication Date: 2020-05-03
• Subjects: Environmental variable -- expected improvement -- Kriging -- personalized decision -- robust optimization -- sequential design; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2019.1630866
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: Optimization problems with both control variables and environmental variables often arise in quality engineering. This article introduces a personalized optimization strategy to handle such problems when the environmental variables can be observed or measured. Unlike traditional robust optimization, personalized optimization aims to find the values of control variables that yield the optimal value of the objective function for given values of environmental variables. Therefore, the solution from personalized optimization, which consists of optimal surfaces defined on the domain of environmental variables, is more reasonable and better than that from robust optimization. The implementation of personalized optimization for expensive black-box computer models is discussed. Based on statistical modeling of computer experiments, we provide two algorithms to sequentially design input values for approximating the optimal surfaces. Numerical examples including a real application show the effectiveness of our algorithms.