Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals

TL;DR

This paper introduces a deep reinforcement learning approach using Proximal Policy Optimization to effectively address the flexible job shop scheduling problem with unpredictable job arrivals, outperforming traditional dispatching rules.

Contribution

It presents a novel DRL-based method that integrates environment-accessible state representations and dispatching rules to handle stochastic job arrivals in FJSP.

Findings

DRL approach outperforms individual dispatching rules across datasets.

Method achieves comparable performance to MILP solutions in heterogeneous scenarios.

Proposed model effectively minimizes total job completion time.

Abstract

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.