  Model-based optimization, in concert with conventional black-box methods, can
quickly solve large-scale combinatorial problems. Recently, quantum-inspired
modeling schemes based on tensor networks have been developed which have the
potential to better identify and represent correlations in datasets. Here, we
use a quantum-inspired model-based optimization method TN-GEO to assess the
efficacy of these quantum-inspired methods when applied to realistic problems.
In this case, the problem of interest is the optimization of a realistic
assembly line based on BMW's currently utilized manufacturing schedule. Through
a comparison of optimization techniques, we found that quantum-inspired
model-based optimization, when combined with conventional black-box methods,
can find lower-cost solutions in certain contexts.
