Abstract
In this paper, we confront a variant of the cutting-stock problem with multiple objectives. It is an actual problem of an industry that manufactures plastic rolls under customers’ demands. The starting point is a solution calculated by a heuristic algorithm, termed SHRP that aims mainly at optimizing the two main objectives, i.e. the number of cuts and the number of different patterns; then the proposed multi-objective genetic algorithm tries to optimize other secondary objectives such as changeovers, completion times of orders weighted 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 and also that it offers a number of non dominated solutions, so that the expert can chose one of them according to his preferences at the time of cutting the orders of a set of customers.
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Varela, R., Muñoz, C., Sierra, M., González-Rodríguez, I. (2008). Improving Cutting-Stock Plans with Multi-objective Genetic Algorithm. In: Filipe, J., Shishkov, B., Helfert, M., Maciaszek, L.A. (eds) Software and Data Technologies. ICSOFT ENASE 2007 2007. Communications in Computer and Information Science, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88655-6_25
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DOI: https://doi.org/10.1007/978-3-540-88655-6_25
Publisher Name: Springer, Berlin, Heidelberg
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