A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Abstract

In sustainable supply chain networks, companies are obligated to have a systematic decision support system in place to help it adopt right decisions at right times. Among strategic decisions, supplier selection and evaluation outranks other decisions in terms of importance due to its long-term impacts. Besides, the adoption of such strategic decision entails exploring several factors that contribute to the complexity of decision making in the supply chain. For the purpose of solving non-linear regression problems, a novel neural network technique known as least squaresupport vector machine (LS-SVM) with maximum generalization ability has successfully been implemented. However, the performance quality of the LS-SVM is recognized to notoriously vary depending on the rigorous selection of its parameters. Therefore, in this paper, a continuous general variable neighborhood search (CGVNS) which is an effective meta-heuristic algorithm to solve the real world engineering continuous optimization problems is proposed to be integrated with LS-SVM. The CGVNS is hybridized in our novel integrated LS-SVM and CGVNS model, to tune the parameters of the LS-SVM to better estimate performance rating of supplier selection and evaluation problem. To demonstrate the improved performance of our proposed integrated model, a real data set from a case study of a supplier selection and evaluation problem is presented in a cosmetics industry. Additionally, comparative evaluations between our proposed model and the conventional techniques, namely nonlinear regression, multi-layer perceptron (MLP) neural network and LS-SVM is provided. The experimental results simply manifest the outperformance of our proposed model in terms of estimation accuracy and effective prediction.

Green Supply Chain Performance Prediction Using a Bayesian Belief Network

“…Comprehensive supply chain performance measurement has been the focus of some of the existing literature. Supplier performance evaluation and study of appropriate performance measures have received special attention from some researchers [34,35]. Most of these studies have focused on measuring supplier performance, and also focused on their roles in the supply chain.…”

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

“…where, is the kernel function parameter. The LS-SVM has widely been applied to engineering applications [28][29][30]; for instance, data fitting of small samples [31], electrical energy consumption forecasting [32], curing thermal process [33], cosmetics productions [34], river water pollution [35], pipeline critical deposition velocity prediction [36], forecasting in civil engineering [37].…”

IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery

“…Sidaoui and Sadouni [38] propose a VNS for multiclass clustering using the carrier vector method, enhancing multiclass classifier performance for high-dimensional problems. Vahdani et al [39] and Yazdani et al [40] apply VNS for optimizing SVM in nonlinear regression problems, utilizing the general method of variable environments. Also, a novel metaheuristic based on TS and VNS is presented and applied for the web service selection problem [41].…”