Industry-relevant optimization tasks in the physical sciences are often "hierarchical, noisy, multifidelity 1,2 , multi-objective , high-dimensional , and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints." Existing benchmark datasets , while very useful, typically are single-objective, single-fidelity, low-dimensional, and ignore or simplify the influence of noise. The inclusion of heteroskedastic noise in a surrogate model enables us to establish a "Turing test" scenario where the surrogate model is virtually identical to the actual simulation. This approach bridges the gap between lowcost surrogate function evaluations using benchmark datasets and costly, real-world objective function evaluations by considering a multi-objective, multi-fidelity, and high-dimensional task while accounting for heteroskedastic noise.