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

2026-05-21

AI Summary

This study addresses the challenge of solving flexible job shop scheduling problems under stochastic job arrivals and high combinatorial complexity, which hinders the effectiveness of traditional methods. The authors propose an event-driven deep reinforcement learning framework based on the Proximal Policy Optimization (PPO) algorithm, wherein a lightweight multilayer perceptron agent dynamically selects the best action from a set of established dispatching rules during execution to minimize total makespan. A key innovation lies in restricting the decision space to well-known dispatching rules and designing a state representation directly extractable from the environment, thereby enhancing both learning efficiency and practical deployability. Experimental results demonstrate that the proposed approach consistently outperforms individual dispatching rules across varying job arrival rates and machine heterogeneity levels, and closely approaches the performance of mixed-integer linear programming in highly heterogeneous settings.

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.

Problem

• Flexible Job Shop Scheduling
• Random Job Arrivals
• Combinatorial Complexity
• Scheduling Optimization

Innovation

• Deep Reinforcement Learning
• Flexible Job Shop Scheduling
• Random Job Arrivals
• Dispatching Rules
• Proximal Policy Optimization