灵活作业车间调度问题 (FJSP) 是一组作业到机器的最佳分配。 FJSP: 中仍然存在两个主要挑战，即未来工作的不可预测性和问题, 的组合复杂性，这使得传统的混合整数线性规划求解器难以解决。本文提出了一种基于事件的 \gls{DRL} 方法来解决具有随机作业到达的 FJSP。具体来说，, 我们采用近端策略优化算法，并使用轻量级多层感知器来训练 \gls{DRL} 代理，以最大限度地缩短所有作业的总完成时间。我们将状态表示设计为可从环境,直接访问，并将学习代理限制为从一组完善的调度规则中进行选择。模拟表明，我们的 \gls{DRL} 方法优于具有不同异质性和作业到达率的数据集上的任何单独调度规则。我们将 \gls{DRL} 与到达触发的混合整数线性规划解决方案进行基准测试，并表明我们的方法实现了良好的性能，特别是当数据集异构时。
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.
科目: 人工智能 (cs.AI); 优化与控制 (math.OC)
Subjects: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)