mlsanityData to 14 Oct 2026

Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems

arXiv 2601.11189 ↗First posted 16 Jan 2026cs.AIcited by 0

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

Abstract (arXiv)

This paper proposes a policy-based deep reinforcement learning hyper-heuristic framework for solving the Job Shop Scheduling Problem. The hyper-heuristic agent learns to switch scheduling rules based on the system state dynamically. We extend the hyper-heuristic framework with two key mechanisms. First, action prefiltering restricts decision-making to feasible low-level actions, enabling low-level heuristics to be evaluated independently of environmental constraints and providing an unbiased assessment. Second, a commitment mechanism regulates the frequency of heuristic switching. We investigate the impact of different commitment strategies, from step-wise switching to full-episode commitment, on both training behavior and makespan. Additionally, we compare two action selection strategies at the policy level: deterministic greedy selection and stochastic sampling. Computational experiments on standard JSSP benchmarks demonstrate that the proposed approach outperforms traditional heuristics, metaheuristics, and recent neural network-based 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 4 of its 44 references that are in the map. First posted on arXiv 16 Jan 2026.

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  1. 2026Flexible Manufacturing Systems Intralogistics: Dynamic Optimization of AGVs and Tool Sharing Using Coloured-Timed Petri Nets and Actor-Critic RL with Actions Masking8 Jan

Other references · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
  3. 2020Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learningcited by 10

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  3. 2026Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
  4. 2024Offline reinforcement learning for job-shop scheduling problems
  5. 2024Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
  6. 2024Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
  7. 2026Variational Approach for Job Shop Scheduling
  8. 2026Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals