Design of sustainable supply chain networks for industrial production with the consideration of carbon emission reduction

With the increasing awareness of consumers’ low-carbon preferences and the implementation of government energy-saving and emission reduction policies, designing supply chain networks solely based on economic benefits is no longer sufficient to meet the development needs of enterprises. Moreover, the drawbacks of traditional supply chain network design that only considers the goal of maximizing profits or minimizing costs are becoming increasingly prominent. To this end, this study proposes a sustainable supply chain network structure for industrial production, which includes a dual-objective planning function model that maximizes profits and minimizes emissions, and a closed-loop supply chain network framework based on the product life cycle. The optimal solution of the function is obtained by using the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) based on elite strategies after the improvement of immune operators. Empirical results show that the average values of time index (198), convergence measure (0.34) and interval index (623) in the improved NSGA-II algorithm are better than in other algorithms and this algorithm has clearer explanation for the decisions it made. It is also effective in helping companies choose emission reduction technologies and low-cost production facilities. With the improved algorithm, the total cost as well as carbon emissions can be reduced. The research method provides a new perspective for carbon emission management and sustainable development, and offers practical solutions for enterprises to reduce their carbon footprint and allocate resources reasonably in production and supply chain management. It helps to improve supply chain resilience and achieve sustainable development goals under economic development.