  Complex real-life routing challenges can be modeled as variations of
well-known combinatorial optimization problems. These routing problems have
long been studied and are difficult to solve at scale. The particular setting
may also make exact formulation difficult. Deep Learning offers an increasingly
attractive alternative to traditional solutions, which mainly revolve around
the use of various heuristics. Deep Learning may provide solutions which are
less time-consuming and of higher quality at large scales, as it generally does
not need to generate solutions in an iterative manner, and Deep Learning models
have shown a surprising capacity for solving complex tasks in recent years.
Here we consider a particular variation of the Capacitated Vehicle Routing
(CVRP) problem and investigate the use of Deep Learning models with explicit
memory components. Such memory components may help in gaining insight into the
model's decisions as the memory and operations on it can be directly inspected
at any time, and may assist in scaling the method to such a size that it
becomes viable for industry settings.

Neural and Evolutionary Computing