Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap
The growing demand for real-time, data-driven decision-making in complex and dynamic systems is placing increasing pressure on traditional Operational Research (OR) methodologies. Reinforcement learning (RL) has emerged as a complementary approach, offering strong learning and computational capabilities for sequential decision-making in dynamic and uncertain environments. Recent research shows an increasing interest in integrating RL with OR to address dynamic decision-making problems, enhance heuristic and exact methods for combinatorial optimization, and support the development of digital replicas of operational systems. The overarching goal across these efforts is to leverage the learn...
| Authors | Yahan Lu, Dongyang Xia, Nursen Aydin et al. |
|---|---|
| Primary category | math.OC |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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