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Sustainable supply chain risk prediction based on a meta-heuristic algorithm in cross-border E-commerce

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
data-science
social-computing

Abstract

The drastic development of cross-border e-commerce has driven the supply chain industry economy forward, and sustainable supply chain (SSC) has promoted the economy nowadays, but its risk cannot be easily assessed. The problem of SSC risk prediction is investigated in this paper, and the associated risk is assessed using a multi-objective regression analysis technique. Firstly, the quantification of meta-heuristic abstract indicators is completed using the Analytic Hierarchy Process (AHP) method; secondly, the support vector machine (SVM) model is optimized by a meta-heuristic algorithm to improve assessment accuracy, with the result indicating that the established generic algorithm—particle swarm optimization—SVM (GA-PSO-SVM) model performs better under root mean square error (RMSE) indicators and the error is reduced by 20%; finally, the method provides intelligent risk assessment and technical support for the sustainable supply chain management (SSCM) of cross-border e-commerce and provides new solutions for the development of such enterprises.
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