Genetic Modelling Approach for the Two-Dimensional Variable-Sized Cutting Stock Problem

P. Terán Viadero, M. A. Carravilla, J. F. Oliveira, F. J. Martín Campo, A. Alonso Ayuso

This work addresses the Two-Dimensional Variable-Sized Cutting Stock Problem (2DVSCSP), motivated by a real industrial application in honeycomb cardboard production. In this setting, stock dimensions are not predefined and must be determined jointly with cutting patterns to minimise material usage under exact two-stage guillotine constraints. This introduces strong interdependencies between decisions, increasing problem complexity.

We propose a genetic modelling framework solved using a Biased Random Key Genetic Algorithm. Solutions are represented through a problem-specific chromosome encoding and a decoder that simultaneously defines stock sizes and cutting patterns, ensuring feasibility by construction. Computational results show that the approach provides high-quality solutions and adapts well to different settings.

Keywords: Cutting Stock Problem, Variable-Sized Stock, Biased Random Key Genetic Algorithm

Heuristics and Metaheuristics
September 2, 2026  12:40 PM
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