Determining the parameters of dual-card kanban system: an integrated multicriteria and artificial neural network methodology

Araz O. U., Eski O., Araz C.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, vol.38, pp.965-977, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38
  • Publication Date: 2008
  • Doi Number: 10.1007/s00170-007-1138-1
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.965-977
  • Keywords: kanban, multicriteria decision making, simulation metamodeling, artificial neural networks, JIT PRODUCTION CONTROL, OPTIMUM NUMBER, SIMULATION, TIME, OPTIMIZATION, DESIGN, MULTIPRODUCT, ALLOCATION, MULTIITEM, ALGORITHM
  • Dokuz Eylül University Affiliated: Yes


In this study, we proposed a methodology for determining the design parameters of kanban systems. 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 to evaluate kanban combinations. In order to reflect the decision maker's point of view, different weight structures are used to find the optimum design parameters. The proposed methodology is applied to a case problem and the results are presented. We also performed several experiments on different types of problems to show the effectiveness of the methodology.