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

2026-05-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Optimization & Control Systems · Depth: Expert, quick

Summary

An event-based Deep Reinforcement Learning (DRL) approach is proposed to address the Flexible Job Shop Scheduling Problem (FJSP) with random job arrivals. This problem involves optimally allocating jobs to machines, complicated by unpredictable arrivals and combinatorial complexity, which makes it intractable for conventional solvers. The DRL method employs the Proximal Policy Optimization algorithm and lightweight Multi-Layer Perceptrons to train an agent, aiming to minimize the total completion time of all jobs. The state representation is directly accessible from the environment, and the agent selects from established dispatching rules. Simulations demonstrate that this DRL approach outperforms individual dispatching rules across datasets with varying heterogeneity and job arrival rates, achieving good performance, especially with heterogeneous datasets, when benchmarked against an arrival-triggered mixed-integer linear programming solution.

Key takeaway

For Machine Learning Engineers or Operations Researchers tasked with optimizing dynamic job shop scheduling, this DRL approach offers a robust solution. You should consider implementing an event-based DRL system, particularly for environments with random job arrivals and heterogeneous datasets, as it demonstrably outperforms traditional dispatching rules and competes well with arrival-triggered MILP solutions. Focus on designing state representations that are directly accessible and training agents to select from established dispatching rules to minimize total completion time.

Key insights

DRL with PPO and MLPs effectively solves FJSP with random arrivals by learning dispatching rule selection.

Principles

• DRL can overcome combinatorial complexity in dynamic scheduling.
• Learning to select dispatching rules outperforms individual rules.

Method

An event-based DRL approach using Proximal Policy Optimization and Multi-Layer Perceptrons trains an agent to select from dispatching rules, minimizing total job completion time in FJSP.

In practice

• Apply DRL to dynamic scheduling with random job arrivals.
• Benchmark DRL against MILP for heterogeneous datasets.

Topics

• Deep Reinforcement Learning
• Flexible Job Shop Scheduling
• Proximal Policy Optimization
• Multi-Layer Perceptrons
• Combinatorial Optimization
• Production Scheduling

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.