Implementation of MO-E-EQI was in R (R Core Team, 2022). Sequential design maximizing MO-E-EQI was implemented in the R package MOEEQI and DiceOptim . To incorporate noisy observations into our algorithm, GPs were fitted using the km function in the DiceKriging (Roustant et al., 2021) package providing the noise.var argument, specifying the corresponding variance for each observational point. For algorithm comparison, qNEHVI, qNParEGO were implemented in Python using Botorch and TSEMO was implement through Summit . Gaussian process models were trained using HeteroskedasticSingleTaskGP in Botorch. An interactive algorithm comparisons app was designed using R Shiny , see Appendix E. The code for this study can be found online: processes/MO-E-EQI.