SOM-Based Clustering and Optimization of Production

被引：0

作者：

Potocnik, Primoz ^{[1
]}

Berlec, Tomaz ^{[1
]}

Starbek, Marko ^{[1
]}

Govekar, Edvard ^{[1
]}

机构：

[1] Univ Ljubljana, Fac Mech Engn, Ljubljana 61000, Slovenia

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PT I | 2011年 / 363卷

关键词：

production optimization; clustering; SUM neural network; k-means clustering; hierarchical clustering; GROUP-TECHNOLOGY; CELL-FORMATION; VALIDATION; MODELS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An application of clustering methods for production planning is proposed. Hierarchical clustering, k-means and SUM 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 SUM clustering. In order to make clustering results applicable to industrial production planning, a percentile measure for the interpretation of SUM 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.

引用

页码：21 / 30

页数：10

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