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Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions

30 September 2022
Rindranirina Ramamonjison
Haley Li
Timothy T. Yu
Shiqi He
Vishnu Rengan
Amin Banitalebi-Dehkordi
Zirui Zhou
Yong Zhang
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Abstract

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains.

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