We address the solution of Mixed Integer Linear Programming (MILP) models with strong relaxations that are derived from Dantzig-Wolfe decompositions and allow a pseudo-polynomial pricing algorithm. We exploit their network-flow characterization and provide a framework based on column generation, reduced-cost variable-fixing, and a highly asymmetric branching scheme that allows us to take advantage of the potential of the current MILP solvers. We apply our framework to a variety of cutting and packing problems from the literature. The efficiency of the framework is proved by extensive computational experiments, in which a significant number of open instances could be solved to proven optimality for the first time.
@article{arxiv.2105.14961,
title = {Exact solution of network flow models with strong relaxations},
author = {Vinícius L. de Lima and Manuel Iori and Flávio K. Miyazawa},
journal= {arXiv preprint arXiv:2105.14961},
year = {2021}
}