  Dynamic dispatching aims to smartly allocate the right resources to the right
place at the right time. Dynamic dispatching is one of the core problems for
operations optimization in the mining industry. Theoretically, deep
reinforcement learning (RL) should be a natural fit to solve this problem.
However, the industry relies on heuristics or even human intuitions, which are
often short-sighted and sub-optimal solutions. In this paper, we review the
main challenges in using deep RL to address the dynamic dispatching problem in
the mining industry.
