A multi-criteria decision making procedure based on neural networks for kanban allocation

Araz Ö., ESKİ Ö., Araz C.

ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS, cilt.3973, ss.898-905, 2006 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3973
  • Basım Tarihi: 2006
  • Dergi Adı: ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.898-905
  • Manisa Celal Bayar Üniversitesi Adresli: Hayır

Özet

In this study, we proposed a methodology for determining optimal number of kanbans for each station in a JIT manufacturing system. In this methodology, a backpropagation neural network is used in order to generate simulation meta-models, and a multi-criteria decision making technique (TOPSIS) is employed in order to evaluate kanban combinations with respect to the relative importance of the performance measures. The proposed methodology is applied to a case problem and the results are presented. The results show that the methodology can solve this type of problems effectively and efficiently.