An approach based on ANFIS input selection and modeling for supplier selection problem

Authors:
Ali Fuat GüNeri;Tijen Ertay;Atakan YüCel
Affiliations:
Department of Industrial Engineering, Yildiz Technical University, Besiktas 34349, Istanbul, Turkey;Department of Managerial Engineering, Istanbul Technical University, Macka 34367, Istanbul, Turkey;Department of Industrial Engineering, Yildiz Technical University, Besiktas 34349, Istanbul, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 14
Cited 3

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Extensions of the TOPSIS for group decision-making under fuzzy environment

Fuzzy Sets and Systems
An integrated multicriteria decision-making methodology for outsourcing management

Computers and Operations Research
An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Digital Signal Processing
A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks

Expert Systems with Applications: An International Journal
Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan

Engineering Applications of Artificial Intelligence
An adaptive neuro-fuzzy inference system for bridge risk assessment

Expert Systems with Applications: An International Journal
DJIA stock selection assisted by neural network

Expert Systems with Applications: An International Journal
An expert system for predicting aeration performance of weirs by using ANFIS

Expert Systems with Applications: An International Journal
An adaptive fusion algorithm based on ANFIS for radar/infrared system

Expert Systems with Applications: An International Journal
A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks

Expert Systems with Applications: An International Journal
An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM

Expert Systems with Applications: An International Journal
Forecasting stock market short-term trends using a neuro-fuzzy based methodology

Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms

Expert Systems with Applications: An International Journal

A two-phase case-based distance approach for multiple-group classification problems

Computers and Industrial Engineering
Application of decision-making techniques in supplier selection: A systematic review of literature

Expert Systems with Applications: An International Journal
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Supplier selection is a key task for firms, enabling them to achieve the objectives of a supply chain. Selecting a supplier is based on multiple conflicting factors, such as quality and cost, which are represented by a multi-criteria description of the problem. In this article, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to overcome the supplier selection problem. First, criteria that are determined for the problem are reduced by applying ANFIS input selection method. Then, the ANFIS structure is built using data related to selected criteria and the output of the problem. The proposed method is illustrated by a case study in a textile firm. Finally, results obtained from the ANFIS approach we developed are compared with the results of the multiple regression method, demonstrating that the ANFIS method performed well.