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.
This work has been partially supported by the Principality of Asturias Government under Research Contract FC-06-BP04-021.
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Muñoz, C., Sierra, M., Puente, J., Vela, C.R., Varela, R. (2007). Improving Cutting-Stock Plans with Multi-objective Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_53
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DOI: https://doi.org/10.1007/978-3-540-73053-8_53
Publisher Name: Springer, Berlin, Heidelberg
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