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.