An Optimization-Based Decision-Support Framework for Sustainable Recycled Cotton Utilization in Sock Manufacturing: An Integrated Evaluation of Economic, Environmental, and Operational Performance

The increasing adoption of sustainable production practices in the textile industry has highlighted the need to evaluate recycled raw materials from economic, environmental, and operational perspectives. This study develops an optimization-based decision-support framework to determine the preferred recycled cotton ratio in sock manufacturing by separately minimizing production cost and carbon emissions under operational constraints. The framework was developed using real industrial data from a single sock manufacturer, with the recycled cotton ratio (r) defined as the decision variable and wastage rate, defect rate, and production time incorporated as production-performance constraints. The model was implemented in Python 3.13.15 and solved using the Sequential Least Squares Programming (SLSQP) algorithm, with the solutions independently assessed through grid-based global verification and Pareto dominance analysis. Sensitivity, production constraint, raw-material price, electricity price, and break-even analyses were conducted to evaluate the stability of the preferred solution under different production and economic conditions. Under the baseline conditions, r = 1 was the preferred solution for both objectives, reducing production cost by 18.7% and carbon emissions by 34.9% compared with r = 0. The Pareto assessment identified r = 1 as the only non-dominated solution under the baseline parameterization. Although the preferred solution remained stable across the examined sensitivity and price scenarios, the break-even analysis showed that the economic preference shifted from r = 1 to r = 0 when the recycled-to-virgin cotton price ratio approached approximately 0.98. Overall, the proposed framework provides a structured decision-support approach for evaluating recycled cotton utilization by integrating economic, environmental, and operational considerations while identifying the conditions under which the preferred solution remains stable or changes.