Uncertainty Analysis of Trade-offs Between Multiple Responses Using Hypervolume
Document Type
Article
Publication Date
2017
Keywords
global and instance-specific normalized hypervolumes, model parameter uncertainty, multiple response optimization, Pareto front, scaling choices
Digital Object Identifier (DOI)
https://doi.org/10.1002/qre.2193
Abstract
When multiple responses are considered in process optimization, the degree to which they can be simultaneously optimized depends on the optimization objectives and the amount of trade-offs between the responses. The normalized hypervolume of the Pareto front is a useful summary to quantify the amount of trade-offs required to balance performance across the multiple responses. To quantify the impact of uncertainty of the estimated response surfaces and add realism to what future data to expect, 2 versions of the scaled normalized hypervolume of the Pareto front are presented. To demonstrate the variation of the hypervolume distributions, we explore a case study for a chemical process involving 3 responses, each with a different type of optimization goal. Results show that the global normalized hypervolume characterizes the proximity to the ideal results possible, while the instance-specific summary considers the richness of the front and the severity of trade-offs between alternatives. The 2 scaling schemes complement each other and highlight different features of the Pareto front and hence are useful to quantify what solutions are possible for simultaneous optimization of multiple responses.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Quality and Reliability Engineering International, v. 33, issue 8, p. 2343-2360
Scholar Commons Citation
Cao, Yongtao; Lu, Lu; and Anderson-Cook, Christine M., "Uncertainty Analysis of Trade-offs Between Multiple Responses Using Hypervolume" (2017). Mathematics and Statistics Faculty Publications. 145.
https://digitalcommons.usf.edu/mth_facpub/145
