Abstract
Quantum Annealing was previously applied to the vehicle routing problem and the results were promising. For all benchmark instances in the study, optimal results were obtained. However, 100% success rate was not achieved in every case, and tuning the control parameters for larger instances proved cumbersome. This work addresses these remaining difficulties. An empirical approach is taken wherein measurements of run-time behaviour are exploited to transform existing good values of control parameters so that they can be used successfully for other problem instances. The course of this work shows a method which simplifies hand-tuning so that the heuristic performs successfully when applied to larger instances, and also demonstrates a tuning method which establishes control parameter values for instances which belong in broadly defined groupings. In addition, new best known solutions for large-scale instances, and initial results for the distance-constrained variant of the vehicle routing problem are presented.
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