An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Issue 3 (April 2016)

• Record Type: Journal Article
• Authors: Fallahpour, Alireza; Olugu, Ezutah; Musa, Siti; Khezrimotlagh, Dariush; Wong, Kuan
• Is Part Of: Neural computing & applications. Volume 27:Issue 3(2016)
• Page Start: 707
• Page End: 725
• Publication Date: 2016-04
• Subjects: Green supplier selection -- Data envelopment analysis (DEA) -- Artificial intelligence -- Genetic programming (GP) -- Parametric analysis; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1890-3
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers' efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.