Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
This addresses the problem of inefficient resource allocation in mining operations, but it is incremental as it focuses on reviewing existing challenges rather than proposing new solutions.
The paper reviews the challenges of applying deep reinforcement learning to dynamic dispatching in the mining industry, where current methods rely on sub-optimal heuristics or human intuition.
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.