Multi-label Classification for the Generation of Sub-problems in Time-constrained Combinatorial Optimization

Luca Mossina (1) , Emmanuel Rachelson (2) , Daniel Delahaye (3)

1 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
2 DMIA - Département de Mathématiques, Informatique, Automatique
3 ENAC - Ecole Nationale de l'Aviation Civile

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 sub-problem 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.

Mots clés: Machine Learning; Recurrent Problems; Combinatorial Optimization; Mixed Integer Linear Programming; Multi-label Classification; Neurosciences

Domaines: Optimisation et contrôle [math.OC]

Luca Mossina, Emmanuel Rachelson, Daniel Delahaye. Multi-label Classification for the Generation of Sub-problems in Time-constrained Combinatorial Optimization. ICORES 2019, 8th International Conference on Operations Research and Enterprise Systems, Feb 2019, Prague, Czech Republic. pp 133-141; ISBN: 978-989-758-352-0, ⟨10.5220/0007396601330141⟩. ⟨hal-02120128⟩