Continuous high-frequency wood drying, when integrated with a traditional wood finishing line, allows correcting moisture content one piece of lumber at a time in order to improve its value. However, the integration of this precision drying process complicates sawmills logistics. The high stochasticity of lumber properties and less than ideal lumber routing decisions may cause bottlenecks and reduces productivity. To counteract this problem and fully exploit the technology, we propose to use reinforcement learning (RL) for learning continuous drying operation policies. An RL agent interacts with a simulated model of the finishing line to optimize its policies. Our results, based on multiple simulations, show that the learned policies outperform the heuristic currently used in industry and are robust to sudden disturbances which frequently occur in real contexts.

连续高频木材干燥与传统木材精加工线集成时，可以一次校正一块木材的含水量，以提高其价值。然而，这种精密干燥工艺的集成使锯木厂的物流变得复杂。木材特性的高随机性和不太理想的木材路由决策可能会导致瓶颈并降低生产率。为了解决这个问题并充分利用该技术，我们建议使用强化学习（RL）来学习连续干燥操作策略。强化学习代理与终点线的模拟模型交互以优化其策略。我们基于多次模拟的结果表明，学习到的策略优于当前行业中使用的启发式策略，并且对于真实环境中经常发生的突发干扰具有鲁棒性。