@inproceedings{ramamonjison-etal-2022-augmenting,
title = "Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions",
author = "Ramamonjison, Rindra and
Li, Haley and
Yu, Timothy and
He, Shiqi and
Rengan, Vishnu and
Banitalebi-dehkordi, Amin and
Zhou, Zirui and
Zhang, Yong",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.4",
doi = "10.18653/v1/2022.emnlp-industry.4",
pages = "29--62",
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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%0 Conference Proceedings
%T Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions
%A Ramamonjison, Rindra
%A Li, Haley
%A Yu, Timothy
%A He, Shiqi
%A Rengan, Vishnu
%A Banitalebi-dehkordi, Amin
%A Zhou, Zirui
%A Zhang, Yong
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ramamonjison-etal-2022-augmenting
%X 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.
%R 10.18653/v1/2022.emnlp-industry.4
%U https://aclanthology.org/2022.emnlp-industry.4
%U https://doi.org/10.18653/v1/2022.emnlp-industry.4
%P 29-62
Markdown (Informal)
[Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions](https://aclanthology.org/2022.emnlp-industry.4) (Ramamonjison et al., EMNLP 2022)
ACL
- Rindra Ramamonjison, Haley Li, Timothy Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-dehkordi, Zirui Zhou, and Yong Zhang. 2022. Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 29–62, Abu Dhabi, UAE. Association for Computational Linguistics.