制造和物流领域的调度团队终于有了应对随机订单到达的实用方案——DRL 智能体比传统调度规则更灵活，尤其适合订单类型多样的场景，做生产排程优化的可以直接参考实验方法。
该论文提出了一种基于事件的深度强化学习方法，用于解决柔性作业车间调度问题中随机订单到达的挑战。研究采用近端策略优化算法和轻量级多层感知机训练智能体，以最小化所有作业的总完成时间。智能体从一组成熟的调度规则中选择动作，状态表示直接从环境中获取。仿真结果表明，该方法在不同异质性和订单到达率的数据集上均优于任何单一调度规则，并且在数据集异质性高时表现尤为出色。
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
The Flexible Job Shop Scheduling Problem (FJSP) is the optimal allocation of a set of jobs to machines. Two primary challenges persist in FJSP: the unpredictable arrival of future jobs and the combinatorial complexity of the problem, rendering it intractable for conventional mixed-integer linear programming solvers. This paper proposes an event-based \gls{DRL} approach to solve FJSP with random job arrivals. Specifically, we employ the Proximal Policy Optimization algorithm and use lightweight Multi-Layer Perceptrons to train the \gls{DRL} agent for minimizing the total completion time of all jobs. We design the state representation to be directly accessible from the environment, and limit the learning agent to selecting from among a set of well-established dispatching rules. Simulations show that our \gls{DRL} approach outperforms any of the individual dispatching rules on datasets with varying heterogeneity and job arrival rates. We benchmark our \gls{DRL} against an arrival-triggered mixed-integer linear programming solution and show that our method achieves good performance especially when the datasets are heterogeneous.