Michele Monaci

Prof. Dr. Michele Monaci

orcid

Research Focus & Metrics

Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2002 in European Journal of Operational Research
Verified
Journal Article
Closed access
ALAndrea Lodi
Andrea Lodi
SMSilvano Martello
Silvano Martello
MMMichele Monaci
Michele Monaci

1 August 2003 in INFORMS Journal on Computing
Verified
Journal Article
Closed access
SMSilvano Martello
Silvano Martello
MMMichele Monaci
Michele Monaci
DVDaniele Vigo
Daniele Vigo
We consider the problem of orthogonally packing a given set of rectangular items into a given strip, by minimizing the overall height of the packing. The problem is NP-hard in the strong sense, and finds several applications in cutting and packing. We propose a new relaxation that produces good lower bounds and gives information to obtain effective heuristic algorithms. These results are used in a branch-and-bound algorithm, which was able to solve test instances from the literature involving up to 200 items.

1 December 2017 in Operations Research
Verified
Journal Article
Closed access
MFMatteo Fischetti
Matteo Fischetti
ILIvana Ljubić
Ivana Ljubić
MMMichele Monaci
Michele Monaci
MSMarkus Sinnl
Markus Sinnl
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. The online appendix is available at https://doi.org/10.1287/opre.2017.1650 .

1 April 2006 in Discrete Applied Mathematics
Verified
Journal Article
Closed access
ACAlberto Caprara
Alberto Caprara
MMMichele Monaci
Michele Monaci
PTPaolo Toth
Paolo Toth
PGPier Luigi Guida
Pier Luigi Guida

1 January 2009
Verified
Book Chapter
Closed access
PDF file
MFMatteo Fischetti
Matteo Fischetti
MMMichele Monaci
Michele Monaci

4 May 2016 in Computational Optimization and Applications
Verified
Journal Article
Closed access
PDF file
PBPietro Belotti
Pietro Belotti
PBPierre Bonami
Pierre Bonami
MFMatteo Fischetti
Matteo Fischetti
ALAndrea Lodi
Andrea Lodi
MMMichele Monaci
Michele Monaci
DSDomenico Salvagnin
Domenico Salvagnin