强化学习在运筹学中的应用：技术综述与实践路线图

中文摘要 AI 辅助

复杂动态系统中对实时、数据驱动决策的需求日益增长，给传统运筹学（OR）方法论带来了越来越大的压力。强化学习（RL）作为一种互补方法应运而生，为动态和不确定环境中的序贯决策提供了强大的学习和计算能力。近期研究表明，将RL与OR相结合以解决动态决策问题、增强组合优化的启发式和精确方法，以及支持运营系统数字孪生体的开发，正引起越来越多的关注。这些努力的总体目标是利用RL的学习能力来强化传统OR算法，提高解的质量、计算效率和鲁棒性。鉴于集成方法和应用场景的多样性，迫切需要一份系统且技术细节详尽的综述，阐明RL如何赋能OR方法。为填补这一空白，本文对RL在赋能OR中扮演的三个关键角色进行了结构化综述：（i）解决动态环境中的序贯决策问题；（ii）作为端到端求解方法或作为集成在组合优化问题的启发式和精确OR方法中的组件；（iii）通过与数字孪生系统集成促进扩展现实分析。我们批判性地综合了这些角色的最新进展，强调了它们的优势、实施要求、局限性和挑战。最后，基于这些见解，我们勾勒了未来研究的路线图，以进一步推进RL与OR的方法论和实践集成。

英文摘要

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 learning capabilities of RL to strengthen traditional OR algorithms, improving solution quality, computational efficiency, and robustness. Given the diversity of integration approaches and application settings, there is a clear need for a systematic and technically detailed review of how RL empowers OR methods. To address this gap, this paper presents a structured review of three key roles that RL plays in empowering OR: (i) solving sequential decision-making problems in dynamic environments, (ii) serving as an end-to-end solution method or as a component integrated within heuristic and exact OR methods for combinatorial optimization problems, and (iii) facilitating extended reality analysis through integration with digital twin systems. We critically synthesize recent advances across these roles, highlighting their advantages, implementation requirements, limitations, and challenges. Finally, based on these insights, we outline a roadmap for future research to further advance the methodological and practical integration of RL and OR.

发表机构

• Delft University of Technology（代尔夫特理工大学）
• University of Warwick（华威大学）

机构由 AI 辅助整理，请以论文原文为准。