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Learning to handle parameter perturbations in Combinatorial Optimization : an application to facility location

Andrea Lodi, Luca Mossina and Emmanuel Rachelson

Article (2020)

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Cite this document: Lodi, A., Mossina, L. & Rachelson, E. (2020). Learning to handle parameter perturbations in Combinatorial Optimization : an application to facility location. EURO Journal on Transportation and Logistics, 9(4). doi:10.1016/j.ejtl.2020.100023
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Abstract

We present an approach to couple the resolution of Combinatorial Optimization problems with methods from Machine Learning. Specifically, our study is framed in the context where a reference discrete optimization problem is given and there exist data for many variations of such reference problem (historical or simulated) along with their optimal solution. Those variations can be originated by disruption but this is not necessarily the case. We study how one can exploit these to make predictions about an unseen new variation of the reference instance. The methodology is composed by two steps. We demonstrate how a classifier can be built from these data to determine whether the solution to the reference problem still applies to a perturbed instance. In case the reference solution is only partially applicable, we build a regressor indicating the magnitude of the expected change, and conversely how much of it can be kept for the perturbed instance. This insight, derived from a priori information, is expressed via an additional constraint in the original mathematical programming formulation. We present the methodology through an application to the classical facility location problem and we provide an empirical evaluation and discuss the benefits, drawbacks and perspectives of such an approach. Although it cannot be used in a black-box manner, i.e., it has to be adapted to the specific application at hand, we believe that the approach developed here is general and explores a new perspective on the exploitation of past experience in Combinatorial Optimization.

Uncontrolled Keywords

Mathematical programming, Machine learning, Recurrent problems

Open Access document in PolyPublie
Subjects: 2950 Mathématiques appliquées > 2959 Mathématiques des télécommunications
Department: Département de mathématiques et de génie industriel
Research Center: Autre
Funders: CERC in Data Science for Real-Time Decision-Making, ISAE-SUPAERO foundation
Date Deposited: 05 Apr 2022 14:34
Last Modified: 06 Apr 2022 01:20
PolyPublie URL: https://publications.polymtl.ca/9272/
Document issued by the official publisher
Journal Title: EURO Journal on Transportation and Logistics (vol. 9, no. 4)
Publisher: Elsevier
Official URL: https://doi.org/10.1016/j.ejtl.2020.100023

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