Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
For operations research practitioners, this work provides a DRL method that handles dynamic job arrivals in FJSP, but it is incremental as it combines existing techniques (PPO, dispatching rules) rather than introducing a new paradigm.
This paper proposes an event-based deep reinforcement learning approach using PPO and MLPs to solve the flexible job shop scheduling problem with random job arrivals, outperforming individual dispatching rules and achieving good performance compared to MILP on heterogeneous datasets.
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.