mlsanityData to 14 Oct 2026

An efficient deep reinforcement learning environment for flexible job-shop scheduling

arXiv 2509.07019 ↗First posted 7 Sep 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The Flexible Job-shop Scheduling Problem (FJSP) is a classical combinatorial optimization problem that has a wide-range of applications in the real world. In order to generate fast and accurate scheduling solutions for FJSP, various deep reinforcement learning (DRL) scheduling methods have been developed. However, these methods are mainly focused on the design of DRL scheduling Agent, overlooking the modeling of DRL environment. This paper presents a simple chronological DRL environment for FJSP based on discrete event simulation and an end-to-end DRL scheduling model is proposed based on the proximal policy optimization (PPO). Furthermore, a short novel state representation of FJSP is proposed based on two state variables in the scheduling environment and a novel comprehensible reward function is designed based on the scheduling area of machines. Experimental results on public benchmark instances show that the performance of simple priority dispatching rules (PDR) is improved in our scheduling environment and our DRL scheduling model obtains competing performance compared with OR-Tools, meta-heuristic, DRL and PDR scheduling methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 29 references that are in the map. First posted on arXiv 7 Sep 2025.

Builds on

2 of 29 references.

References · 2

  1. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
  2. 2023Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learningcited by 20

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  1. 2026Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
  2. 2024Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
  3. 2026Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints
  4. 2026RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
  5. 2024Learning to Solve Job Shop Scheduling under Uncertainty
  6. 2026Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
  7. 2024Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing
  8. 2026Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems