Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at the intersection of LLMs and optimization to create a Decision Optimization CoPilot (DOCP) - an AI tool designed to assist any decision maker, interacting in natural language to grasp the business problem, subsequently formulating and solving the corresponding optimization model. This paper outlines our DOCP vision and identifies several fundamental requirements for its implementation. We describe the state of the art through a literature survey and experiments using ChatGPT. We show that a) LLMs already provide substantial novel capabilities relevant to a DOCP, and b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We also see this work as a call to action to bring together the LLM and optimization communities to pursue our vision, thereby enabling much more widespread improved decision-making.

翻译：长期以来，显著简化面向现实商业问题的优化模型构建，一直是推动数学优化更广泛应用于重要商业和社会决策的主要目标。大型语言模型（LLMs）的最新能力为实现这一目标提供了及时机遇。因此，我们提出在LLMs与优化交叉领域进行研究，以创建决策优化协同驾驶仪（DOCP）——一种旨在辅助任何决策者的AI工具，通过自然语言交互理解业务问题，进而构建并求解相应的优化模型。本文概述了我们的DOCP愿景，并指出了其实现的若干基本要求。我们通过文献综述及使用ChatGPT的实验描述了当前技术水平，表明：a) LLMs已具备与DOCP相关的大量新颖能力，b) 仍存在重大研究挑战有待解决。我们还提出了克服这些差距的潜在研究方向。我们将此工作视为一项行动号召，旨在汇聚LLM与优化社区共同追求我们的愿景，从而推动更广泛、更优化的决策制定。