The Benders decomposition algorithm: A literature review

By Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei. European Journal of Operational Research, 2017

876 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/f94b892c496dbfd4ea96e78654131e85f3dad467

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Greger Ottosson
2. John N. Hooker
3. Gianni Codato
4. Matteo Fischetti

In Computational Theory and Mathematics

1. Qiuwei Wu
2. Greger Ottosson
3. John N. Hooker
4. Jessica Davies

In Computer Networks and Communications

1. Greger Ottosson
2. John N. Hooker
3. Jessica Davies
4. Matteo Fischetti
5. Gianni Codato
6. Teodor Gabriel Crainic
7. Michel Gendreau
8. Walter Rei

Citations in the corpus

• Logic-based Benders decomposition (2003, positive): “An excellent survey of developments in Benders decomposition methods, including LBBD, can be found in [19].”
• Local flexibility markets: Literature review on concepts, models and clearing methods (2020, methodology): “Benders decomposition is a classic primal decomposition method [159], which is widely used in the literature.”