Metaheuristic approaches to solving combinatorial optimization problems have many attractions.
They sidestep the issue of combinatorial explosion; they return good results; they are often
conceptually simple and straight forward to implement. There are also shortcomings. Optimal
solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of
experimentation; and conceptual differences between metaheuristics make absolute comparisons
of performance difficult. There is also the difficulty of configuration of the algorithm - the process
of identifying precise values for the parameters which control the optimization process.
Quantum annealing is a metaheuristic which is the quantum counterpart of the well known
classical Simulated Annealing algorithm for combinatorial optimization problems. This research
investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult
problem of practical significance within industries such as logistics and workforce scheduling. The
work devises spin encoding schemes for routing and scheduling problem domains, enabling an
effective quantum annealing algorithm which locates new solutions to widely used benchmarks.
The performance of the metaheuristic is further improved by the development of an enhanced
tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further
enhanced by taking advantage of multiprocessor environments, using threading techniques to
parallelize the optimization workload. The work also shows quantum annealing applied successfully
in an industrial setting to generate solutions to complex scheduling problems, results which
created extra savings over an incumbent optimization technique. Components of the intellectual
property rendered in this latter effort went on to secure a patent-protected status