Sustainable Supplier Selection by a New Hybrid Support Vector-model based on the Cuckoo Optimization Algorithm


1 Industerial Engineering, University of Tehran

2 , University of Tehran


For assessing and selecting sustainable suppliers, this study considers a triple-bottom-line approach, including profit, people and planet, and regards business operations, environmental effects along with social responsibilities of the suppliers. Diverse metrics are acquainted with measure execution in these three issues. This study builds up a new hybrid intelligent model, namely COA-LS-SVM, for taking performance variations of the sustainable suppliers quantified by the performance index. The presented artificial intelligent (AI) model is introduced in light of a new combination of least squares-support vector machine (LS-SVM) and cuckoo optimization algorithm (COA). The LS-SVM is used in regards to the mapping capacity amongst performance index and its causative input criteria. The COA is presented to advance LS-SVM tuning parameters. In this exploration, an illustrative database comprising of 80 historical cases is gathered to set up the presented intelligence system. In the light of experimental results, the presented COA-LS-SVM can effectively illustrate performance index’s variances since it has accomplished relatively low statistical metrics. Therefore, the proposed hybrid AI framework can be a promising approach to help the supply chain decision-makers in sustainable supply chain management (SSCM).