IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v4y2023i2d10.1007_s43069-023-00209-4.html
   My bibliography  Save this article

PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic

Author

Listed:
  • Cecil Ash

    (Dalhousie University)

  • Uday Venkatadri

    (Dalhousie University)

  • Claver Diallo

    (Dalhousie University)

  • Peter Vanberkel

    (Dalhousie University)

  • Ahmed Saif

    (Dalhousie University)

Abstract

The COVID-19 pandemic has struck health service providers around the world with dire shortages, inflated prices, and volatile demand of personal protective equipment (PPE). This paper discusses supply chain resilience in the context of a Canadian provincial healthcare provider during the COVID-19 pandemic. A multi-period multi-objective mixed-integer programming model is presented for PPE supply planning under disruption risk. The deterministic formulation is extended to consider both two-stage and multi-stage uncertainty in the supply, price, and demand of PPE using stochastic programming (SP) and chance-constrained programming (CCP). The first objective is to minimize a risk measure of the stochastic total cost, either its Expected Value (EV) or its Value-at-Risk (VaR), and the second objective is to minimize the maximum shortage of any product in any time period. The $$\epsilon$$ ϵ -constraint method is used to generate sets of Pareto-optimal solutions and analyze the trade-off between these two competing objectives. Numerical experiments are conducted to analyze the efficacy of emergency inventory and increased inventory levels as risk mitigation strategies. We consider uncertainty scenarios based on plausible and actual pandemic trajectories seen around the world during the COVID-19 pandemic including single-wave, two-wave, and exponential growth.

Suggested Citation

  • Cecil Ash & Uday Venkatadri & Claver Diallo & Peter Vanberkel & Ahmed Saif, 2023. "PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic," SN Operations Research Forum, Springer, vol. 4(2), pages 1-29, June.
    • Handle: RePEc:spr:snopef:v:4:y:2023:i:2:d:10.1007_s43069-023-00209-4
    DOI: 10.1007/s43069-023-00209-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-023-00209-4
    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/s43069-023-00209-4?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. 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.
    2. Kamalahmadi, Masoud & Parast, Mahour Mellat, 2017. "An assessment of supply chain disruption mitigation strategies," International Journal of Production Economics, Elsevier, vol. 184(C), pages 210-230.
    3. Jiho Yoon & Srinivas Talluri & Hakan Yildiz & William Ho, 2018. "Models for supplier selection and risk mitigation: a holistic approach," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3636-3661, May.
    4. David Simchi‐Levi & He Wang & Yehua Wei, 2018. "Increasing Supply Chain Robustness through Process Flexibility and Inventory," Production and Operations Management, Production and Operations Management Society, vol. 27(8), pages 1476-1491, August.
    5. Shipra Agrawal & Yichuan Ding & Amin Saberi & Yinyu Ye, 2012. "Price of Correlations in Stochastic Optimization," Operations Research, INFORMS, vol. 60(1), pages 150-162, February.
    6. Lichun Chen & Elise Miller-Hooks, 2012. "Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport," Transportation Science, INFORMS, vol. 46(1), pages 109-123, February.
    7. Ehrgott, Matthias & Ide, Jonas & Schöbel, Anita, 2014. "Minmax robustness for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 239(1), pages 17-31.
    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. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    10. Kamalahmadi, Masoud & Parast, Mahour Mellat, 2016. "A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 116-133.
    11. 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.
    12. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    13. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    14. Brian Tomlin, 2006. "On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks," Management Science, INFORMS, vol. 52(5), pages 639-657, May.
    15. Cardoso, Sónia R. & Paula Barbosa-Póvoa, Ana & Relvas, Susana & Novais, Augusto Q., 2015. "Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty," Omega, Elsevier, vol. 56(C), pages 53-73.
    16. Sahebjamnia, Navid & Torabi, S. Ali & Mansouri, S. Afshin, 2018. "Building organizational resilience in the face of multiple disruptions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 63-83.
    17. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.
    18. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
    19. Quddus, Md Abdul & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Yu, Fei & Bian, Linkan, 2018. "A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network," International Journal of Production Economics, Elsevier, vol. 195(C), pages 27-44.
    20. Torabi, S.A. & Baghersad, M. & Mansouri, S.A., 2015. "Resilient supplier selection and order allocation under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 22-48.
    21. Matthias Ehrgott, 2005. "Multicriteria Optimization," Springer Books, Springer, edition 0, number 978-3-540-27659-3, September.
    22. Ho-Yin Mak & Zuo-Jun Shen, 2012. "Risk diversification and risk pooling in supply chain design," IISE Transactions, Taylor & Francis Journals, vol. 44(8), pages 603-621.
    23. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    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. 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.
    2. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    3. Aghajani, Mojtaba & Ali Torabi, S. & Altay, Nezih, 2023. "Resilient relief supply planning using an integrated procurement-warehousing model under supply disruption," Omega, Elsevier, vol. 118(C).
    4. Lohmer, Jacob & Bugert, Niels & Lasch, Rainer, 2020. "Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study," International Journal of Production Economics, Elsevier, vol. 228(C).
    5. Fattahi, Mohammad & Govindan, Kannan & Maihami, Reza, 2020. "Stochastic optimization of disruption-driven supply chain network design with a new resilience metric," International Journal of Production Economics, Elsevier, vol. 230(C).
    6. 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.
    7. 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.
    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. Lydia Novoszel & Tina Wakolbinger, 2022. "Meta-analysis of Supply Chain Disruption Research," SN Operations Research Forum, Springer, vol. 3(1), pages 1-25, March.
    10. João Pires Ribeiro & Ana Paula F. D. Barbosa-Póvoa, 2023. "A responsiveness metric for the design and planning of resilient supply chains," Annals of Operations Research, Springer, vol. 324(1), pages 1129-1181, May.
    11. 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.
    12. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    13. Cheramin, Meysam & Saha, Apurba Kumar & Cheng, Jianqiang & Paul, Sanjoy Kumar & Jin, Hongyue, 2021. "Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    14. 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).
    15. Wei Xu & Dong-Ping Song, 2022. "Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies," Operational Research, Springer, vol. 22(3), pages 2343-2371, July.
    16. Zhalechian, M. & Torabi, S. Ali & Mohammadi, M., 2018. "Hub-and-spoke network design under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 20-43.
    17. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.
    18. Vimal K.E.K & Simon Peter Nadeem & Mahadharsan Ravichandran & Manavalan Ethirajan & Jayakrishna Kandasamy, 2022. "Resilience strategies to recover from the cascading ripple effect in a copper supply chain through project management," Operations Management Research, Springer, vol. 15(1), pages 440-460, June.
    19. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    20. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.

    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:snopef:v:4:y:2023:i:2:d:10.1007_s43069-023-00209-4. 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.