Article: Support vector machine learning-based green secure supply chain management and optimisation Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2026 Vol.18 No.3/4 pp.355 - 368 Abstract: Green supply chain management (GSCM) is crucial for sustainable business practices, emphasising environmental impact mitigation and resource efficiency. In this context, assessing supply chain risks is vital to ensure resilience and stability. This study explores existing literature to develop a comprehensive framework for supply chain risk assessment that integrates GSCM principles. The aim is to identify and evaluate risks from both environmental and operational angles. Firstly, to implement this framework, data were gathered from industry experts and practitioners via structured questionnaires. Secondly, these data were analysed using a machine learning algorithm based on support vector machines (SVM), enabling the construction of a risk assessment model that encompasses both conventional and green-specific risks. Lastly, experimental analysis is performed to test the model's effectiveness. Results indicate that the SVM-based model not only accurately forecasts potential risks but also demonstrates high computational efficiency during training, which underscores the significance of embedding GSCM principles within supply chain risk management. Inderscience Publishers - linking academia, business and industry through research

Title: Support vector machine learning-based green secure supply chain management and optimisation

Authors: Mingjiang Zhao; Jiming Dai; Jiyang Kang; He Li

Addresses: State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang, 110000, China ' State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang, 110000, China ' State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang, 110000, China ' Yingkou Power Supply Company of State Grid Liaoning Electric Power Supply Co., Ltd., Yingkou, 115000, China

Abstract: Green supply chain management (GSCM) is crucial for sustainable business practices, emphasising environmental impact mitigation and resource efficiency. In this context, assessing supply chain risks is vital to ensure resilience and stability. This study explores existing literature to develop a comprehensive framework for supply chain risk assessment that integrates GSCM principles. The aim is to identify and evaluate risks from both environmental and operational angles. Firstly, to implement this framework, data were gathered from industry experts and practitioners via structured questionnaires. Secondly, these data were analysed using a machine learning algorithm based on support vector machines (SVM), enabling the construction of a risk assessment model that encompasses both conventional and green-specific risks. Lastly, experimental analysis is performed to test the model's effectiveness. Results indicate that the SVM-based model not only accurately forecasts potential risks but also demonstrates high computational efficiency during training, which underscores the significance of embedding GSCM principles within supply chain risk management.

Keywords: supply chain; green and risk assessment; support vector machine; SVM; management; optimisation.

DOI: 10.1504/IJIIDS.2026.155310

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.3/4, pp.355 - 368

Received: 17 Apr 2024
Accepted: 14 Mar 2025
Published online: 30 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Support vector machine learning-based green secure supply chain management and optimisation

Keep up-to-date