  Noisy intermediate-scale quantum (NISQ) hardware is almost universally
incompatible with full-scale optimization problems of practical importance
which can have many variables and unwieldy objective functions. As a
consequence, there is a growing body of literature that tests quantum
algorithms on miniaturized versions of problems that arise in an operations
research setting. Rather than taking this approach, we investigate a problem of
substantial commercial value, multi-truck vehicle routing for supply chain
logistics, at the scale used by a corporation in their operations. Such a
problem is too complex to be fully embedded on any near-term quantum hardware
or simulator; we avoid confronting this challenge by taking a hybrid workflow
approach: we iteratively assign routes for trucks by generating a new binary
optimization problem instance one truck at a time. Each instance has $\sim
2500$ quadratic binary variables, putting it in a range that is feasible for
NISQ quantum computing, especially quantum annealing hardware. We test our
methods using simulated annealing and the D-Wave Hybrid solver as a
place-holder in wait of quantum hardware developments. After feeding the
vehicle routes suggested by these runs into a highly realistic classical supply
chain simulation, we find excellent performance for the full supply chain. Our
work gives a set of techniques that can be adopted in contexts beyond vehicle
routing to apply NISQ devices in a hybrid fashion to large-scale problems of
commercial interest.
