Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

自第二次世界大战以来，运营研究（OR）一直是决策的核心，如今，不同平台上的业务交互已改变了业务动态，带来了高度的不确定性。为了对业务具有可持续发展的愿景，公司需要在每个阶段都有适当的决策流程，包括详细的细节。我们的研究回顾并调查了决策支持系统（DSS）领域中的现有研究，以及如何将人工智能（AI）功能集成到OR中。我们的审查结果显示了AI如何在运筹学领域为决策做出了贡献。这篇评论介绍了AI，DSS和OR的协同作用，差异和重叠。此外，根据开发DSS的方法对文献进行了说明，并提供了基础理论。该分类主要分为两类，即理论构建和基于应用程序的方法，以及基于AI，DSS和OR区域的分类法。在这篇综述中，根据预后能力，大数据集的开发，考虑的因素数量，学习能力的发展以及决策框架中的验证对过去的研究进行了校准。鉴于当今不确定性时代的智能决策，本文提出了与预测和学习，决策和优化有关的差距和未来的研究机会。理论和管理意义在讨论部分阐述，以证明研究问题的合理性。