Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management sciences (OR/MS), which have long offered conceptual and methodological foundations for sequential decision-making under uncertainty. At the same time, recent advances in deep learning, including feedforward neural networks, LSTMs, transformers, and deep reinforcement learning, have expanded the scope of data-driven modeling and opened new possibilities for large-scale decision systems. This tutorial presents an OR/MS-centered perspective on deep learning for sequential decision-making under uncertainty. Its central premise is that deep learning is valuable not as a replacement for optimization, but as a complement to it. Deep learning brings adaptability and scalable approximation, whereas OR/MS provides the structural rigor needed to represent constraints, recourse, and uncertainty. The tutorial reviews key decision-making foundations, connects them to the major neural architectures in modern AI, and discusses leading approaches to integrating learning and optimization. It also highlights emerging impact in domains such as supply chains, healthcare and epidemic response, agriculture, energy, and autonomous operations. More broadly, it frames these developments as part of a wider transition from predictive AI toward decision-capable AI and highlights the role of OR/MS in shaping the next generation of integrated learning--optimization systems.

翻译：人工智能（AI）正日益超越预测范畴，转向支持复杂、不确定和动态环境中的决策。这一转变与运筹学与管理科学（OR/MS）产生了自然的交叉——后者长期以来为不确定性下的序贯决策提供了概念与方法论基础。与此同时，深度学习的最新进展（包括前馈神经网络、LSTM、Transformer和深度强化学习）拓展了数据驱动建模的边界，为大规模决策系统开辟了新可能。本教程从运筹学与管理学的视角，系统阐述深度学习在不确定性下序贯决策中的应用。其核心观点是：深度学习并非取代优化，而是为其提供补充——深度学习带来适应性与可扩展近似能力，而OR/MS则提供表征约束、应急策略与不确定性所需的结构严谨性。教程回顾了序贯决策的关键基础，将其与现代AI中的主要神经架构相关联，并讨论了整合学习与优化的主流方法。此外，还重点展示了供应链、医疗与流行病应对、农业、能源及自主系统等领域的潜在影响。更广泛而言，本研究将这些发展视为从预测型AI向决策型AI转型的重要组成部分，并强调了OR/MS在塑造下一代学习-优化集成系统中的关键作用。