A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection

A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection Supplier evaluation and selection is a complicated multiple criteria decision-making process which affects supply chain management (SCM) directly. Recent studies emphasize that artificial intelligence approaches obtain better performance than conventional methods in evaluating the suppliers’ performance and determining the best suppliers. Hence, this study proposes a new robust genetic-based intelligent approach, namely gene expression programming (GEP), to improve the supplier selection process for a supply chain and to cope with the drawback of the other intelligent approaches in this area. The applicability of this method was exhibited by a case study in the textile manufacturing industry. To show the performance of the mathematical-genetic model, comparisons with four intelligent techniques, namely multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM), were performed. The results derived from the intelligent approaches were compared by using a collected dataset from a textile factory. The obtained results demonstrated that first the GEP-based model provides a mathematical model for the suppliers’ performance based on the determined criteria, and the developed GEP model is more accurate than the four other intelligent models in terms of accuracy in performance estimation. In addition, to verify the validity of the developed model, different statistical tests were used and the results showed that the GEP model is statistically powerful. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Fuzzy Systems Springer Journals

A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Operations Research, Management Science
ISSN
1562-2479
eISSN
2199-3211
D.O.I.
10.1007/s40815-017-0324-z
Publisher site
See Article on Publisher Site

Abstract

Supplier evaluation and selection is a complicated multiple criteria decision-making process which affects supply chain management (SCM) directly. Recent studies emphasize that artificial intelligence approaches obtain better performance than conventional methods in evaluating the suppliers’ performance and determining the best suppliers. Hence, this study proposes a new robust genetic-based intelligent approach, namely gene expression programming (GEP), to improve the supplier selection process for a supply chain and to cope with the drawback of the other intelligent approaches in this area. The applicability of this method was exhibited by a case study in the textile manufacturing industry. To show the performance of the mathematical-genetic model, comparisons with four intelligent techniques, namely multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM), were performed. The results derived from the intelligent approaches were compared by using a collected dataset from a textile factory. The obtained results demonstrated that first the GEP-based model provides a mathematical model for the suppliers’ performance based on the determined criteria, and the developed GEP model is more accurate than the four other intelligent models in terms of accuracy in performance estimation. In addition, to verify the validity of the developed model, different statistical tests were used and the results showed that the GEP model is statistically powerful.

Journal

International Journal of Fuzzy SystemsSpringer Journals

Published: May 31, 2017

References

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