Title: Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network
Authors: Golam Kabir; M. Ahsan Akhtar Hasin
Addresses: Department of Industrial and Production Engineering (IPE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh ' Department of Industrial and Production Engineering (IPE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh
Abstract: A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. To efficiently control the inventory items and to determine the suitable ordering policies for them, multi-criteria inventory classification is used. The objective of this research is to develop a multi-criteria inventory classification model through integration of fuzzy analytic hierarchy process (FAHP) and artificial neural network approach. FAHP is used to determine the relative weights of the attributes or criteria using Chang's extent analysis and to classify inventories into different categories. Various structures of multi-layer feed-forward back-propagation neural networks have been analysed and the optimal one with the minimum mean absolute percentage of error between the measured and the predicted values have been selected. To accredit the proposed model, it is implemented for 351 raw materials of switchgear section of Energypac Engineering Limited, a large power engineering company of Bangladesh.
Keywords: ANNs; artificial neural networks; FAHP; fuzzy AHP; analytical hierarchy process; multicriteria inventory classification; inventory control; raw materials; switchgear.
International Journal of Industrial and Systems Engineering, 2013 Vol.14 No.1, pp.74 - 103
Available online: 29 Mar 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article