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Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns).
A linear programming approach to the cutting-stock problem
P. C. Gilmore and R. E. Gomory · 1961
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Algorithms for the vehicle routing and scheduling problem with time window constraints
M. Solomon · 1987
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A new optimization algorithm for the vehicle routing problem with time windows
Jacques Desrosiers; Marius Solomon; Martin Desrochers · 1992
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A reinforcement learning approach to job-shop scheduling
Wei Zhang and Thomas G Dietterich · 1995
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Accelerating strategies in column generation methods for vehicle routing and crew scheduling problems
Guy Desaulniers, Jacques Desrosiers, and Marius Solomon · 1999
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The vrp with time windows
J.-F. Cordeau, G. Desaulniers, J. Desrosiers, and F. Soumis · 2002
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Airline crew scheduling
Cynthia Barnhart, Amy M Cohn, Ellis L Johnson, Diego Klabjan, George L Nemhauser, and Pamela H Vance · 2003
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Cutting stock problems - in column generation
Hatem Ben Amor and Jose Valerio de Carvalho · 2004
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Column generation - a primer in column generation
Guy Desaulniers, Jacques Desrosiers, and Solomon M. Marius · 2006
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
Cited alongside, same era.
Learning combinatorial optimization algorithms over graphs
Hanjun Dai, Elias Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Cited alongside, same era.
Bpplib: a library for bin packing and cutting stock problems
Maxence Delorme, Manuel Iori, and Silvano Martello · 2018
Cited alongside, same era.
Exact combinatorial optimization with graph convolutional neural networks
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, and Andrea Lodi · 2019
Cited alongside, same era.
A new branch-and-price-and-cut algorithm for one-dimensional bin-packing problems
Lijun Wei, Zhixing Luo, Roberto Baldacci, and Andrew Lim · 2020
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Improved flow-based formulations for the skiving stock problem
John Martinovic, Maxence Delorme, Manuel Iori, Guntram Scheithauer, and Nico Strasdat · 2020
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Curriculum learning for reinforcement learning domains: A framework and survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E Taylor, and Peter Stone · 2020
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Reinforcement learning with combinatorial actions: An application to vehicle routing
Arthur Delarue, Ross Anderson, and Christian Tjandraatmadja · 2020
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2021
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Reinforcement learning for integer programming: Learning to cut
Yunhao Tang, Shipra Agrawal, and Yuri Faenza · 2020
Cited alongside, same era.
Combining reinforcement learning and constraint programming for combinatorial optimization
Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau, Isabeau Prémont-Schwarz, and Andre Cire · 2020
Cited alongside, same era.
Enhanced pseudo-polynomial formulations for bin packing and cutting stock problems
Maxence Delorme and Manuel Iori · 2020
Cited alongside, same era.
Machine-learning–based column selection for column generation
Mouad Morabit, Guy Desaulniers, and Andrea Lodi · 2021
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Reinforcement learning for combinatorial optimization: A survey
Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, and Evgeny Burnaev · 2021
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković · 2021
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