Intelligent Collaborative Sustainable Supply Chain Optimization: An Evolutionary Transfer Learning Framework

Authors

  • Yuhan Guo School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou, China
  • Jiawei Lin School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou, China
  • Yiyang Qian School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou, China

DOI:

https://doi.org/10.23055/ijietap.2025.32.4.10631

Keywords:

Cross-Network Collaboration; Sustainable Supply Chain; Carbon Trading; Evolutionary Transfer Learning

Abstract

A multi-objective, multi-product, and cross-network sustainable supply chain network design problem is considered. We propose a mixed-integer linear programming model for cross-network collaboration, considering carbon trading, and design an evolutionary transfer learning algorithm to solve the model. The proposed model incorporates economic and environmental objectives, integrating carbon pricing, partner sharing, and logistics collaboration to account for carbon emissions within economic costs, thus achieving multi-objective optimization. The evolutionary transfer learning algorithm incorporates evolutionary procedures and a Markov decision-making process to solve the model efficiently. Extensive experiments based on real-world data are constructed, and the results demonstrate that the proposed method enhances problem-solving efficiency and accuracy across various scenarios while enhancing its stability and robustness. Additionally, case studies of different scales are demonstrated to verify the strong transferability of the proposed method.

Published

2025-08-11

How to Cite

Guo, Y., Lin, J., & Qian, Y. (2025). Intelligent Collaborative Sustainable Supply Chain Optimization: An Evolutionary Transfer Learning Framework. International Journal of Industrial Engineering: Theory, Applications and Practice, 32(4). https://doi.org/10.23055/ijietap.2025.32.4.10631

Issue

Section

Supply Chain Management