IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v25y2025i2d10.1007_s12351-025-00928-y.html
   My bibliography  Save this article

A bi-objective robust optimization model to bolster a resilient medical supply chain in case of the ripple effect

Author

Listed:
  • Gökhan Özçelik

    (Karadeniz Technical University)

  • Fatma Betül Yeni

    (Karadeniz Technical University)

  • Beren Gürsoy Yılmaz

    (Karadeniz Technical University)

  • Ömer Faruk Yılmaz

    (Karadeniz Technical University)

Abstract

The medical supply chain (MSC) plays a crucial role in ensuring that drugs, vaccines, gloves, and other medical products are delivered to the right place at the right times and in the required quantity. For this reason, the viability of MSC is vital in case of an external risk such as the coronavirus (COVID-19) pandemic. The COVID-19 pandemic is a type of ripple effect that has devastating effect on supply chain performance. With that in mind, to the best of our knowledge, this study explores a bi-objective MSC design problem under uncertainty caused by the ripple effect for the first time. Accordingly, a generic bi-objective robust optimization model is fundamentally developed to represent the addressed problem mathematically by considering the uncertainty sourcing from pandemic. To obtain applicable results, a real case study is considered for MSC design in Istanbul/Turkey as a practical contribution by validating the optimization model. Furthermore, a set of scenarios are generated by placing an emphasis on the decrease in capacity utilization rates and the increase in product demand due to the pandemic. A computational study is conducted through scenarios and risk mitigation strategies to reveal managerial insights by combining the strategic and operational level decisions regarding the MSC network. The improved augmented ϵ-constrained (AUGMECON2) method is employed to obtain diversified Pareto-optimal solutions for all problems. Several comparison metrics are employed to further analyze the solutions from different perspectives. According to the computational results attained by extensive experiments, a unified strategy is proposed to achieve MSC resiliency. Besides, solving large sized problem instances through the proposed methodology is highlighted as the main limitation of this study.

Suggested Citation

  • Gökhan Özçelik & Fatma Betül Yeni & Beren Gürsoy Yılmaz & Ömer Faruk Yılmaz, 2025. "A bi-objective robust optimization model to bolster a resilient medical supply chain in case of the ripple effect," Operational Research, Springer, vol. 25(2), pages 1-42, June.
    • Handle: RePEc:spr:operea:v:25:y:2025:i:2:d:10.1007_s12351-025-00928-y
    DOI: 10.1007/s12351-025-00928-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-025-00928-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-025-00928-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Florios, Kostas & Mavrotas, George, 2014. "Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems," MPRA Paper 105074, University Library of Munich, Germany.
    2. Niknejad, A. & Petrovic, D., 2014. "Optimisation of integrated reverse logistics networks with different product recovery routes," European Journal of Operational Research, Elsevier, vol. 238(1), pages 143-154.
    3. Yılmaz, Ömer Faruk & Guan, Yongpei & Yılmaz, Beren Gürsoy & Yeni, Fatma Betül & Özçelik, Gökhan, 2025. "A comprehensive methodology combining machine learning and unified robust stochastic programming for medical supply chain viability," Omega, Elsevier, vol. 133(C).
    4. Emily L. Tucker & Mark S. Daskin & Burgunda V. Sweet & Wallace J. Hopp, 2020. "Incentivizing resilient supply chain design to prevent drug shortages: policy analysis using two- and multi-stage stochastic programs," IISE Transactions, Taylor & Francis Journals, vol. 52(4), pages 394-412, April.
    5. De Rosa, Vincenzo & Gebhard, Marina & Hartmann, Evi & Wollenweber, Jens, 2013. "Robust sustainable bi-directional logistics network design under uncertainty," International Journal of Production Economics, Elsevier, vol. 145(1), pages 184-198.
    6. Ivanov, Dmitry & Pavlov, Alexander & Pavlov, Dmitry & Sokolov, Boris, 2017. "Minimization of disruption-related return flows in the supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 503-513.
    7. Yin, Xuecheng & Büyüktahtakın, İ. Esra & Patel, Bhumi P., 2023. "COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk," European Journal of Operational Research, Elsevier, vol. 304(1), pages 255-275.
    8. Raj, Alok & Mukherjee, Abheek Anjan & de Sousa Jabbour, Ana Beatriz Lopes & Srivastava, Samir K., 2022. "Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned," Journal of Business Research, Elsevier, vol. 142(C), pages 1125-1139.
    9. Tadeusz Sawik, 2017. "A portfolio approach to supply chain disruption management," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1970-1991, April.
    10. Gökhan Özçelik & Ömer Faruk Yılmaz & Fatma Betül Yeni, 2021. "Robust optimisation for ripple effect on reverse supply chain: an industrial case study," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 245-264, January.
    11. Dmitry Ivanov, 2018. "Revealing interfaces of supply chain resilience and sustainability: a simulation study," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3507-3523, May.
    12. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    13. Alexandre Dolgui & Dmitry Ivanov & Maxim Rozhkov, 2020. "Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1285-1301, March.
    14. Tadeusz Sawik, 2016. "On the risk-averse optimization of service level in a supply chain under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 98-113, January.
    15. Alok Raj & Abheek Anjan Mukherjee & Ana Beatriz Lopes de Sousa Jabbour & Samir K. Srivastava, 2022. "Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned," Post-Print hal-04275089, HAL.
    16. Abdorrrahman Haeri & Seyyed-Mahdi Hosseini-Motlagh & Mohammad Reza Ghatreh Samani & Marziehsadat Rezaei, 2022. "An integrated socially responsible-efficient approach toward health service network design," Annals of Operations Research, Springer, vol. 319(1), pages 463-516, December.
    17. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2018. "Ripple effect in the supply chain: an analysis and recent literature," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 414-430, January.
    18. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    19. Vipul Jain & Sameer Kumar & Umang Soni & Charu Chandra, 2017. "Supply chain resilience: model development and empirical analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6779-6800, November.
    20. Michael K. Lim & Achal Bassamboo & Sunil Chopra & Mark S. Daskin, 2013. "Facility Location Decisions with Random Disruptions and Imperfect Estimation," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 239-249, May.
    21. Gholami-Zanjani, Seyed Mohammad & Klibi, Walid & Jabalameli, Mohammad Saeed & Pishvaee, Mir Saman, 2021. "The design of resilient food supply chain networks prone to epidemic disruptions," International Journal of Production Economics, Elsevier, vol. 233(C).
    22. Mohammad Reza Ghatreh Samani & Seyyed-Mahdi Hosseini-Motlagh, 2019. "An enhanced procedure for managing blood supply chain under disruptions and uncertainties," Annals of Operations Research, Springer, vol. 283(1), pages 1413-1462, December.
    23. Ivanov, Dmitry & Pavlov, Alexander & Sokolov, Boris, 2014. "Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics," European Journal of Operational Research, Elsevier, vol. 237(2), pages 758-770.
    24. Chen, Kexin & Pun, Chi Seng & Wong, Hoi Ying, 2023. "Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 84-98.
    25. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Homaei, Shamim, 2023. "Design of control strategies to help prevent the spread of COVID-19 pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 219-238.
    26. Zahiri, Behzad & Zhuang, Jun & Mohammadi, Mehrdad, 2017. "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 109-142.
    27. Songtao Zhang & Panpan Zhang & Min Zhang, 2019. "Fuzzy Emergency Model and Robust Emergency Strategy of Supply Chain System under Random Supply Disruptions," Complexity, Hindawi, vol. 2019, pages 1-10, January.
    28. Rezapour, Shabnam & Farahani, Reza Zanjirani & Pourakbar, Morteza, 2017. "Resilient supply chain network design under competition: A case study," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1017-1035.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    2. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    3. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).
    4. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    5. Liu, Ming & Ding, Yueyu & Chu, Feng & Dolgui, Alexandre & Zheng, Feifeng, 2024. "Robust actions for improving supply chain resilience and viability," Omega, Elsevier, vol. 123(C).
    6. Abdolreza Roshani & Philip Walker-Davies & Glenn Parry, 2024. "Designing resilient supply chain networks: a systematic literature review of mitigation strategies," Annals of Operations Research, Springer, vol. 341(2), pages 1267-1332, October.
    7. Alam, Md Fahim Bin & Tushar, Saifur Rahman & Ahmed, Tazim & Karmaker, Chitra Lekha & Bari, A.B.M. Mainul & de Jesus Pacheco, Diego Augusto & Nayyar, Anand & Islam, Abu Reza Md Towfiqul, 2024. "Analysis of the enablers to deal with the ripple effect in food grain supply chains under disruption: Implications for food security and sustainability," International Journal of Production Economics, Elsevier, vol. 270(C).
    8. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    9. Hanieh Shekarabi & Mohammad Mahdi Vali-Siar & Ashkan Mozdgir, 2024. "Food supply chain network design under uncertainty and pandemic disruption," Operational Research, Springer, vol. 24(2), pages 1-37, June.
    10. Lida Safari & Seyed Jafar Sadjadi & Farzad Movahedi Sobhani, 2024. "Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 27485-27527, November.
    11. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
    12. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry, 2023. "Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments," Omega, Elsevier, vol. 117(C).
    13. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    14. R. Rajesh, 2022. "A novel advanced grey incidence analysis for investigating the level of resilience in supply chains," Annals of Operations Research, Springer, vol. 308(1), pages 441-490, January.
    15. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    16. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    17. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    18. Ming Liu & Hao Tang & Yunfeng Wang & Ruixi Li & Yi Liu & Xin Liu & Yaqian Wang & Yiyang Wu & Yu Wu & Zhijun Sun, 2023. "Enhancing Food Supply Chain in Green Logistics with Multi-Level Processing Strategy under Disruptions," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    19. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    20. Chih-Hung Hsu & Ming-Ge Li & Ting-Yi Zhang & An-Yuan Chang & Shu-Zhen Shangguan & Wan-Ling Liu, 2022. "Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework," Mathematics, MDPI, vol. 10(8), pages 1-35, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:25:y:2025:i:2:d:10.1007_s12351-025-00928-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.