  This paper develops an inherently parallelised, fast, approximate
learning-based solution to the generic class of Capacitated Vehicle Routing
Problems with Time Windows and Dynamic Routing (CVRP-TWDR). Considering
vehicles in a fleet as decentralised agents, we postulate that using
reinforcement learning (RL) based adaptation is a key enabler for real-time
route formation in a dynamic environment. The methodology allows each agent
(vehicle) to independently evaluate the value of serving each customer, and
uses a centralised allocation heuristic to finalise the allocations based on
the generated values. We show that the solutions produced by this method are
significantly faster than exact formulations and state-of-the-art
meta-heuristics, while being reasonably close to optimal in terms of solution
quality. We describe experiments in both the static case (when all customer
demands and time windows are known in advance) as well as the dynamic case
(where customers can pop up at any time during execution). The results with a
single trained model on large, out-of-distribution test data demonstrate the
scalability and flexibility of the proposed approach.
