We investigate the feasibility of deploying Deep-Q based deep reinforcement
learning agents to job-shop scheduling problems in the context of modular
production facilities, using discrete event simulations for the environment.
These environments are comprised of a source and sink for the parts to be
processed, as well as (several) workstations. The agents are trained to
schedule automated guided vehicles to transport the parts back and forth
between those stations in an optimal fashion. Starting from a very simplistic
setup, we increase the complexity of the environment and compare the agents'
performances with well established heuristic approaches, such as
first-in-first-out based agents, cost tables and a nearest-neighbor approach.
We furthermore seek particular configurations of the environments in which the
heuristic approaches struggle, to investigate to what degree the Deep-Q agents
are affected by these challenges. We find that Deep-Q based agents show
comparable performance as the heuristic baselines. Furthermore, our findings
suggest that the DRL agents exhibit an increased robustness to noise, as
compared to the conventional approaches. Overall, we find that DRL agents
constitute a valuable approach for this type of scheduling problems.

本文研究了在离散事件模拟环境下，使用基于 Deep-Q 的深度强化学习代理来解决模块化生产设施中的作业车间调度问题。研究发现，相比于传统启发式和成本表等方法，基于 DRL 的代理相当，并且具有更高的噪声稳健性，对于这种类型的调度问题值得尝试。