Abstract
Group Technology (GT) is a manufacturing philosophy that explores similarities in product and process design. Starting from a binary machine-part matrix, the objective is to form clusters, made up of families of parts and machine cells aiming to minimize the number of voids and exceptional elements in the cells. Since this is a combinatorial problem, the proposed hybrid genetic algorithm (GA) finds good solutions with a partial greedy population that uses similarities with machines and with parts. A local search k-means based method can recreate cells with small movements among machine assignments. The proposed framework performance presents good results, most of them overcoming literature classic problems solutions, when considering the group effectiveness indicator. The results are presented and discussed.
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Branco, R.M., Rocha, C.R. (2020). Group Technology: Hybrid Genetic Algorithm with Greedy Formation and a Local Search Cluster Technique in the Solution of Manufacturing Cell Formation Problems. In: Anisic, Z., Lalic, B., Gracanin, D. (eds) Proceedings on 25th International Joint Conference on Industrial Engineering and Operations Management – IJCIEOM. IJCIEOM 2019. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-43616-2_7
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