Abstract
In this paper, we confront a variant of the cutting-stock problem with multiple objectives. The starting point is a solution calculated by a heuristic algorithm, termed SHRP, that aims to optimize the two main objectives, i.e. the number of cuts and the number of different patterns. Here, we propose a multi-objective genetic algorithm to optimize other secondary objectives such as changeovers, completion times of orders pondered by priorities and open stacks. We report experimental results showing that the multi-objective genetic algorithm is able to improve the solutions obtained by SHRP on the secondary objectives. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Muñoz, C., Sierra, M., Puente, J., Vela, C. R., & Varela, R. (2007). Improving cutting-stock plans with multi-objective genetic algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4527 LNCS, pp. 528–537). Springer Verlag. https://doi.org/10.1007/978-3-540-73053-8_53
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