Construction of Green Supply Chain Network Optimization Models for Textiles with Carbon Neutrality Goals

Renyi Qiu

Article
2026 / Volume 9 / Pages 366-384
Received 27 July 2025; Accepted 22 September 2025; Published 28 February 2026
https://doi.org/10.31881/TLR.2026.366

Abstract
Given the high carbon intensity and complex supply chains in the textile industry, this paper investigates emission reduction and the trade-off between cost and carbon mitigation under carbon neutrality goals. A multi-objective mixed-integer linear programming (MOMILP) model is developed, integrating carbon trading mechanisms and dynamic demand response. High-carbon activities, including printing and dyeing and chemical fiber production, are considered, while decisions on raw material procurement, green process selection, facility location, production capacity allocation, and logistics routing are coordinated to achieve chain-wide emission reduction. Carbon price fluctuations are incorporated to dynamically model carbon trading, and green incentive constraints encourage the internalization of carbon externalities. The model is solved using an improved Non-dominated Sorting Genetic Al-gorithm II (NSGA-II), with the entropy weight-TOPSIS method applied for multi-attribute decision analysis on the Pareto frontier. Experiments show that the model can reduce costs by approximately 18.6–25.8% and carbon emissions by 22.3–33.2% across various carbon price scenarios. A combined carbon tax + green subsidy policy effectively reduces costs, and the improved algorithm outperforms others in convergence speed and solution di-versity. This study provides a quantitative tool for textile enterprises to balance cost and emission reduction and offers theoretical support for policymakers to design green incentive mechanisms, providing practical guidance for advancing the industry’s carbon-neutral transformation.

Keywords
carbon neutrality, textile supply chain, multi-objective mixed-integer linear programming model, improved NSGA-II algorithm