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A multi-objective optimization approach for designing a sustainable supply chain considering carbon emissions

Author

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  • Amit Kumar

    (University of Delhi)

  • Kaushal Kumar

    (University of Delhi)

Abstract

Greenhouse gases (GHGs) emission has been growing for decades, that has resulted in serious environmental problems. Many researches have focused on production planning in supply chains that are primarily attributed to objectives of cost minimization and profit maximization. There has been limited research concentrated on emission of GHGs (e.g. CO $$_{2}$$ 2 , N $$_{2}$$ 2 O, CH $$_{4}$$ 4 ) from industrial activities. The social concern of air pollution is the motivation towards the proposed study. In this study, we propose an optimization model for designing a sustainable supply chain management (SSCM) network that attempts to reduce carbon emission, improve social benefits and eventually improve customer experience. The optimization model is a multi objective mixed integer linear programming (MILP) formulation. The proposed model is a multi raw-material, multi-product, multi-period, multi-sites model that focuses on improving three dimensions of sustainability: viz. economic, environmental and social. Model performance is demonstrated through numerical experiments. The authors are currently working on solution algorithm for solving larger instances of the proposed model.

Suggested Citation

  • Amit Kumar & Kaushal Kumar, 2024. "A multi-objective optimization approach for designing a sustainable supply chain considering carbon emissions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(5), pages 1777-1793, May.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:5:d:10.1007_s13198-023-02085-x
    DOI: 10.1007/s13198-023-02085-x
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    References listed on IDEAS

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    1. Eskandarpour, Majid & Dejax, Pierre & Miemczyk, Joe & Péton, Olivier, 2015. "Sustainable supply chain network design: An optimization-oriented review," Omega, Elsevier, vol. 54(C), pages 11-32.
    2. Choudhary, Alok & Sarkar, Sagar & Settur, Srikar & Tiwari, M.K., 2015. "A carbon market sensitive optimization model for integrated forward–reverse logistics," International Journal of Production Economics, Elsevier, vol. 164(C), pages 433-444.
    3. Waltho, Cynthia & Elhedhli, Samir & Gzara, Fatma, 2019. "Green supply chain network design: A review focused on policy adoption and emission quantification," International Journal of Production Economics, Elsevier, vol. 208(C), pages 305-318.
    4. Gaigné, C. & Hovelaque, V. & Mechouar, Y., 2020. "Carbon tax and sustainable facility location: The role of production technology," International Journal of Production Economics, Elsevier, vol. 224(C).
    5. Chaabane, A. & Ramudhin, A. & Paquet, M., 2012. "Design of sustainable supply chains under the emission trading scheme," International Journal of Production Economics, Elsevier, vol. 135(1), pages 37-49.
    6. Sadaat Ali Yawar & Stefan Seuring, 2017. "Management of Social Issues in Supply Chains: A Literature Review Exploring Social Issues, Actions and Performance Outcomes," Journal of Business Ethics, Springer, vol. 141(3), pages 621-643, March.
    7. Nurjanni, Kartina Puji & Carvalho, Maria S. & Costa, Lino, 2017. "Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 421-432.
    8. Fatemeh Keshavarz-Ghorbani & Seyed Hamid Reza Pasandideh, 2022. "A Lagrangian relaxation algorithm for optimizing a bi-objective agro-supply chain model considering CO2 emissions," Annals of Operations Research, Springer, vol. 314(2), pages 497-527, July.
    9. Gaigné, C. & Hovelaque, V. & Mechouar, Y., 2020. "Carbon tax and sustainable facility location: The role of production technology," International Journal of Production Economics, Elsevier, vol. 224(C).
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