From Large Language Models and Optimization to Decision Optimization
  CoPilot: A Research Manifesto
Segev Wasserkrug Leonard Boussioux Dick den Hertog Farzaneh Mirzazadeh Ilker Birbil Jannis Kurtz Donato Maragno
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.