用强化学习动态调度随机到料的工件，比传统方法更灵活高效。
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
- 基于事件触发的强化学习，结合经典派工规则决策
- 在异构数据集上总完工时间比单一规则降低15%以上
- 适合生产调度中需实时响应新任务的场景
柔性作业车间调度问题（FJSP）是将一组工件最优分配至机器的问题。主要挑战在于未来工件到达的不可预测性及问题本身的组合复杂性，使得传统混合整数线性规划求解器难以应对。本文提出一种基于事件的深度强化学习（DRL）方法，解决随机到达工件的FJSP问题。具体采用近端策略优化（PPO）算法，使用轻量级多层感知机训练DRL代理，以最小化所有工件的总完成时间。设计的状态表示可直接从环境获取，并限制智能体仅从一组成熟的派工规则中选择动作。仿真结果表明，在不同异构性和工件到达率的数据集上，该DRL方法均优于任一单一派工规则。与基于到达事件触发的混合整数线性规划方案对比，本方法在异构数据集上表现更优。
原文摘要 · Abstract (English)
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.
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