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Solving binary cutting stock with matheuristics using particle swarm optimization and simulated annealing

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Abstract

In last decade, researchers have focused on improving existing methodologies through hybrid algorithms; these are a combination of algorithms between a metaheuristic with other metaheuristic and an exact method, to solve combinatorial optimization problems in the best possible way. This work presents a benchmark of different methodologies to solve the binary cutting stock problem using a column generation framework, this framework is divided into master and subproblem, master problem is solved using a classical integer linear programming, and the subproblem is solved using metaheuristic algorithms (genetic algorithm, simulated annealing and particle swarm optimization). This benchmark analysis is aimed to compare hybrid metaheuristics results with an exact methodology.

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Acknowledgements

This research was supported by a grant from CONACyT.

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Correspondence to Jaime Mora Vargas.

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All authors declare that they have no conflicts of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by V. Loia.

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Sanchez, I.A.L., Vargas, J.M., Santos, C.A. et al. Solving binary cutting stock with matheuristics using particle swarm optimization and simulated annealing. Soft Comput 22, 6111–6119 (2018). https://doi.org/10.1007/s00500-017-2666-8

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