A New General-Purpose Algorithm for Mixed-Integer Bilevel Linear Programs

Fischetti, Matteo; Ljubić, Ivana; Monaci, Michele; Sinnl, Markus

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1287/opre.2017.1650

Title :

A New General-Purpose Algorithm for Mixed-Integer Bilevel Linear Programs

Author(s) :

Fischetti, Matteo [Auteur]
Dipartimento di Ingegneria de l'Informazione [Padova] [DEI]
Ljubić, Ivana [Auteur]
ESSEC Business School
Monaci, Michele [Auteur]
Department of electrical, electronic and information engineering "GUGLIELMO MARCONI" [Bologna] [DEI]
Sinnl, Markus [Auteur]
Integrated Optimization with Complex Structure [INOCS]
Universität Wien = University of Vienna

Journal title :

Operations Research

Pages :

1615 - 1637

Publisher :

INFORMS

Publication date :

2017-12

ISSN :

0030-364X

HAL domain(s) :

Computer Science [cs]/Operations Research [math.OC]

English abstract : [en]

Bilevel optimization problems are very challenging optimization models arising in many important practical contexts, including pricing mechanisms in the energy sector, airline and telecommunication industry, transportation ...
Show more >Bilevel optimization problems are very challenging optimization models arising in many important practical contexts, including pricing mechanisms in the energy sector, airline and telecommunication industry, transportation networks, critical infrastructure defense, and machine learning. In this paper, we consider bilevel programs with continuous and discrete variables at both levels, with linear objectives and constraints (continuous upper level variables, if any, must not appear in the lower level problem). We propose a general-purpose branch-and-cut exact solution method based on several new classes of valid inequalities, which also exploits a very effective bilevel-specific preprocessing procedure. An extensive computational study is presented to evaluate the performance of various solution methods on a common testbed of more than 800 instances from the literature and 60 randomly generated instances. Our new algorithm consistently outperforms (often by a large margin) alternative state-of-the-art methods from the literature, including methods exploiting problem-specific information for special instance classes. In particular, it solves to optimality more than 300 previously unsolved instances from the literature. To foster research on this challenging topic, our solver is made publicly available online.Show less >

Language :

Anglais

Popular science :

Non

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