SOM-Based Clustering and Optimization of Production

Abstract : An application of clustering methods for production planning is proposed. Hierarchical clustering, k-means and SOM clustering are applied to production data from the company KGL in Slovenia. A database of 252 products manufactured in the company is clustered according to the required operations and product features. Clustering results are evaluated with an average silhouette width for a total data set and the best result is obtained by SOM clustering. In order to make clustering results applicable to industrial production planning, a percentile measure for the interpretation of SOM clusters into the production cells is proposed. The results obtained can be considered as a recommendation for production floor planning that will optimize the production resources and minimize the work and material flow transfer between the production cells.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.21-30, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_3〉
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Primož Potočnik, Tomaž Berlec, Marko Starbek, Edvard Govekar. SOM-Based Clustering and Optimization of Production. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.21-30, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_3〉. 〈hal-01571344〉

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