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 incorporate...
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| Published in: | International journal of production economics Vol. 289; p. 109730 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.11.2025
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| Subjects: | |
| ISSN: | 0925-5273 |
| Online Access: | Get full text |
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| Abstract | 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.
•Designing a sustainable closed-loop supply chain network for the cast iron industry.•Developing circular economy practices: recycling, refurbishing, and remanufacturing.•Evaluating sustainability through cost, energy use, emissions, and job creation in the supply chain.•Exploring resilience and safety stock strategies for demand variability in two-stage models.•Addressing uncertainties with a robust stochastic programming approach. |
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| AbstractList | 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.
•Designing a sustainable closed-loop supply chain network for the cast iron industry.•Developing circular economy practices: recycling, refurbishing, and remanufacturing.•Evaluating sustainability through cost, energy use, emissions, and job creation in the supply chain.•Exploring resilience and safety stock strategies for demand variability in two-stage models.•Addressing uncertainties with a robust stochastic programming approach. |
| ArticleNumber | 109730 |
| Author | Rabiee, Meysam Ghasempour Anaraki, Matin Fathollahzadeh, Kasra Saeedi, Mehran |
| Author_xml | – sequence: 1 givenname: Kasra orcidid: 0009-0007-3704-0785 surname: Fathollahzadeh fullname: Fathollahzadeh, Kasra email: fathollahzadeh.k@ut.ac.ir organization: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran – sequence: 2 givenname: Mehran orcidid: 0009-0006-8922-9312 surname: Saeedi fullname: Saeedi, Mehran email: mehran.saeedi@ut.ac.ir organization: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran – sequence: 3 givenname: Matin orcidid: 0009-0001-1968-1382 surname: Ghasempour Anaraki fullname: Ghasempour Anaraki, Matin email: matin.ghasempouranara@uta.edu organization: Department of Industrial, Manufacring & Systems Engineering, The University of Texas Arlington, Arlington, TX, 76019-0017, USA – sequence: 4 givenname: Meysam orcidid: 0000-0001-9602-5912 surname: Rabiee fullname: Rabiee, Meysam email: meysam.rabiee@ucdenver.edu organization: Business School, University of Colorado Denver, Denver, CO, 80202, USA |
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| Keywords | Multi-objective optimization Circular economy Augmented epsilon constraint algorithm Scenario reduction Robust stochastic programming Sustainable supply chain |
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