Sustainable cast iron supply chain network design: Robust multi-objective optimization with scenario reduction via genetic algorithm

The cast iron industry plays a crucial role in global manufacturing, making the shift toward more sustainable and resilient supply chains increasingly important. This study presents a multi-objective optimization model for designing a sustainable cast iron supply chain network. The model incorporates circular economy principles and jointly addresses economic, environmental, and social objectives under uncertainty. Key considerations include minimizing costs, reducing energy consumption and emissions, creating jobs, and developing workforce skills. To manage uncertainty in demand, transportation, and raw material supply, the model combines robust stochastic programming with the augmented epsilon constraint algorithm. A genetic algorithm-based scenario reduction method is applied to reduce computational complexity. A real-world case study illustrates that improving return and recycling rates leads to lower overall costs and environmental impact. While higher return rates slightly increase operational costs, they significantly cut raw material expenses, resulting in net savings. The results also indicate that applying higher penalty coefficients enhances the model's robustness but raises total costs, underscoring the need to balance trade-offs. Moreover, increased adoption of internet of things technologies support job creation but requires additional investment in training programs. Overall, the proposed framework offers valuable insights for companies aiming to improve the sustainability, resilience, and efficiency of their operations. By integrating circular economy strategies with robust decision-making tools, this research contributes a practical approach to strengthening the long-term sustainability and competitiveness of the cast iron industry under uncertainty.