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

• URL: http://arxiv.org/abs/2209.15565v1
• Date: Fri, 30 Sep 2022 16:24:36 GMT
• Title: Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
• Authors: Rindranirina Ramamonjison, Haley Li, Timothy T. Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang
• Abstract summary: 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.
• Score: 7.469806460325306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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