Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866917206360588288 |
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| author | Lassoued, Sofiene Gobachew, Asrat Lier, Stefan Schwung, Andreas |
| author_facet | Lassoued, Sofiene Gobachew, Asrat Lier, Stefan Schwung, Andreas |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11189 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems Lassoued, Sofiene Gobachew, Asrat Lier, Stefan Schwung, Andreas Artificial Intelligence 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 |
| title | Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.11189 |