Abstract

The definition of a suitable neighborhood structure on the solution space is a key step when designing a heuristic for Mixed Integer Programming (MIP). In this paper, we move on from a MIP compact formulation and show how to take advantage of its features to automatically design efficient neighborhoods, without any human analysis. In particular, we use unsupervised learning to automatically identify "good" regions of the search space "around" a given feasible solution. Computational results on compact formulations of three well-known combinatorial optimization problems show that, on large instances, the neighborhoods constructed by our procedure outperform state-of-the-art domain-independent neighborhoods.

Autore Pugliese

• Manni Emanuele , Ghiani Gianpaolo

Tutti gli autori

• G. Ghiani , G. Laporte , E. Manni

Titolo volume/Rivista

COMPUTERS & OPERATIONS RESEARCH

Anno di pubblicazione

2015

ISSN

0305-0548

ISBN

Non Disponibile

Numero di citazioni Wos

3

Ultimo Aggiornamento Citazioni

27/04/2018

Numero di citazioni Scopus

3

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile