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Authors: Luca Mossina 1 ; Emmanuel Rachelson 1 and Daniel Delahaye 2

Affiliations: 1 ISAE-SUPAERO, Université de Toulouse, Toulouse and France ; 2 ENAC, Université de Toulouse, Toulouse and France

Keyword(s): Multi-label Classification, Mixed Integer Linear Programming, Combinatorial Optimization, Recurrent Problems, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Methodologies and Technologies ; Operational Research ; Optimization ; Symbolic Systems

Abstract: This paper addresses the resolution of combinatorial optimization problems presenting some kind of recurrent structure, coupled with machine learning techniques. Stemming from the assumption that such recurrent problems are the realization of an unknown generative probabilistic model, data is collected from previous resolutions of such problems and used to train a supervised learning model for multi-label classification. This model is exploited to predict a subset of decision variables to be set heuristically to a certain reference value, thus becoming fixed parameters in the original problem. The remaining variables then form a smaller subproblem whose solution, while not guaranteed to be optimal for the original problem, can be obtained faster, offering an advantageous tool for tackling time-sensitive tasks.

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Paper citation in several formats:
Mossina, L.; Rachelson, E. and Delahaye, D. (2019). Multi-label Classification for the Generation of Sub-problems in Time-constrained Combinatorial Optimization. In Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-352-0; ISSN 2184-4372, SciTePress, pages 133-141. DOI: 10.5220/0007396601330141

@conference{icores19,
author={Luca Mossina. and Emmanuel Rachelson. and Daniel Delahaye.},
title={Multi-label Classification for the Generation of Sub-problems in Time-constrained Combinatorial Optimization},
booktitle={Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - ICORES},
year={2019},
pages={133-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007396601330141},
isbn={978-989-758-352-0},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - ICORES
TI - Multi-label Classification for the Generation of Sub-problems in Time-constrained Combinatorial Optimization
SN - 978-989-758-352-0
IS - 2184-4372
AU - Mossina, L.
AU - Rachelson, E.
AU - Delahaye, D.
PY - 2019
SP - 133
EP - 141
DO - 10.5220/0007396601330141
PB - SciTePress