Abstract
This paper proposes a new hybrid heuristic to a difficult but frequently occurring problem in the apparel industry: the cut order planning (COP). This problem consists of finding the combination of ordered sizes on the material layers that minimizes total material utilization. The current practice in industry solves COP independently from the two-dimensional layout (TDL) problem; i.e., COP estimates the length of the layout required to cut a particular combination of sizes instead of packing the pieces on the fabric and determining the actual length used. Evidently, this results in a build up of estimation errors; thus increased waste. Herein, COP and TDL are combined into a single problem CT. The resulting problem is modeled and solved using a hybrid heuristic which combines the advantages of population based approaches (genetic algorithms) with those of local search (simulated annealing). The experimental results show the validity of the proposed model, and the sizeable savings it induces when solved using the proposed hybrid heuristic.
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Bouziri, A., M’hallah, R. (2007). A Hybrid Genetic Algorithm for the Cut Order Planning Problem. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_45
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DOI: https://doi.org/10.1007/978-3-540-73325-6_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
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