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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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Submitted on : Wednesday, August 2, 2017 - 11:41:36 AM
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Primož Potočnik, Tomaž Berlec, Marko Starbek, Edvard Govekar. SOM-Based Clustering and Optimization of Production. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.21-30, ⟨10.1007/978-3-642-23957-1_3⟩. ⟨hal-01571344⟩



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