IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v335y2024i1d10.1007_s10479-023-05778-5.html
   My bibliography  Save this article

Optimization of blood supply chains under different supply scenarios

Author

Listed:
  • Benyong Hu

    (University of Electronic Science and Technology of China)

  • Longmei Tian

    (University of Electronic Science and Technology of China)

  • Kangli Zhao

    (University of Electronic Science and Technology of China)

  • Xu Chen

    (University of Electronic Science and Technology of China)

Abstract

Blood is a living tissue of unique value to the human body and has special features with short shelf life, unpredictable supply, and stochastic demand. The efficiency of blood management affects the quality of medical services. Scholars pay more attention to demand uncertainty than to supply uncertainty in blood supply chain management, which leads to a lack of research on supply uncertainty in such management. We take supply uncertainty into account and discuss three different uncertainty scenarios: optimistic, average, and pessimistic supply scenarios. Different supply scenarios will affect not only the quantity of orders but also the inventory freshness. To balance the fairness of the old inventory allocation, we designed a hybrid allocation policy of old stocks by order share and batch allocation of other stocks by hospital priority. The simulation results reveal the direct and cross effects of supply uncertainty, life cycle, and old inventory ratio (OIR) policy on the system-wide outdating rate. First, for the effect of supply uncertainty, when it is smaller, the system’s outdate rate grows with the increase of its intensity, but when it is larger, the outdate rate hardly grows and even decreases in intensity. Especially, when the supply uncertainty is larger, the expected supply scenarios have no significant effect on the outdate rate. Second, for the effect of product shelf life, when the shelf life is longer, the OIR policy can significantly reduce the system’s outdate rate in the optimistic or average supply scenarios and has little impact on the rate in the pessimistic supply scenario. Third, for the effect of the OIR policy, when the intensity of supply uncertainty is smaller, the OIR policy leads to a large increase in the system’s outdate rate as supply uncertainty grows. However, when the intensity of supply uncertainty is larger, the range of increase in system outdate rate further increases in the optimistic scenario. In contrast, in the pessimistic scenario, it will decrease. Besides, the OIR policy will have no significant effect on the outdate rate when the supply uncertainty is smaller.

Suggested Citation

  • Benyong Hu & Longmei Tian & Kangli Zhao & Xu Chen, 2024. "Optimization of blood supply chains under different supply scenarios," Annals of Operations Research, Springer, vol. 335(1), pages 597-633, April.
  • Handle: RePEc:spr:annopr:v:335:y:2024:i:1:d:10.1007_s10479-023-05778-5
    DOI: 10.1007/s10479-023-05778-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05778-5
    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/s10479-023-05778-5?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. Fahimnia, Behnam & Jabbarzadeh, Armin & Ghavamifar, Ali & Bell, Michael, 2017. "Supply chain design for efficient and effective blood supply in disasters," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 700-709.
    2. Steven Nahmias, 1982. "Perishable Inventory Theory: A Review," Operations Research, INFORMS, vol. 30(4), pages 680-708, August.
    3. Roba W. Salem & Mohamed Haouari, 2017. "A simulation-optimisation approach for supply chain network design under supply and demand uncertainties," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1845-1861, April.
    4. Hlioui, Rached & Gharbi, Ali & Hajji, Adnène, 2017. "Joint supplier selection, production and replenishment of an unreliable manufacturing-oriented supply chain," International Journal of Production Economics, Elsevier, vol. 187(C), pages 53-67.
    5. Haijema, René & van Dijk, Nico & van der Wal, Jan & Smit Sibinga, Cees, 2009. "Blood platelet production with breaks: optimization by SDP and simulation," International Journal of Production Economics, Elsevier, vol. 121(2), pages 464-473, October.
    6. K Katsaliaki & S C Brailsford, 2007. "Using simulation to improve the blood supply chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 219-227, February.
    7. Kent D Miller, 1992. "A Framework for Integrated Risk Management in International Business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 23(2), pages 311-331, June.
    8. Anna Nagurney & Amir Masoumi & Min Yu, 2012. "Supply chain network operations management of a blood banking system with cost and risk minimization," Computational Management Science, Springer, vol. 9(2), pages 205-231, May.
    9. Sabitha, Devarajulu & Rajendran, Chandrasekharan & Kalpakam, S. & Ziegler, Hans, 2016. "The value of information sharing in a serial supply chain with AR(1) demand and non-zero replenishment lead times," European Journal of Operational Research, Elsevier, vol. 255(3), pages 758-777.
    10. Gregory P. Prastacos, 1984. "Blood Inventory Management: An Overview of Theory and Practice," Management Science, INFORMS, vol. 30(7), pages 777-800, July.
    11. Roni, Mohammad S. & Eksioglu, Sandra D. & Jin, Mingzhou & Mamun, Saleh, 2016. "A hybrid inventory policy with split delivery under regular and surge demand," International Journal of Production Economics, Elsevier, vol. 172(C), pages 126-136.
    12. Zheng Luo & Xu Chen, 2022. "Ordering policies for heterogeneous platelets demand with unreliable supply and substitution," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(4), pages 919-935, March.
    13. Zhao, Xiande & Xie, Jinxing & Leung, Janny, 2002. "The impact of forecasting model selection on the value of information sharing in a supply chain," European Journal of Operational Research, Elsevier, vol. 142(2), pages 321-344, October.
    14. Geng, Wei & Qiu, Minmin & Zhao, Xiaobo, 2010. "An inventory system with single distributor and multiple retailers: Operating scenarios and performance comparison," International Journal of Production Economics, Elsevier, vol. 128(1), pages 434-444, November.
    15. Duan, Qinglin & Liao, T. Warren, 2013. "A new age-based replenishment policy for supply chain inventory optimization of highly perishable products," International Journal of Production Economics, Elsevier, vol. 145(2), pages 658-671.
    16. Duan, Qinglin & Liao, T. Warren, 2014. "Optimization of blood supply chain with shortened shelf lives and ABO compatibility," International Journal of Production Economics, Elsevier, vol. 153(C), pages 113-129.
    17. Beliën, Jeroen & Forcé, Hein, 2012. "Supply chain management of blood products: A literature review," European Journal of Operational Research, Elsevier, vol. 217(1), pages 1-16.
    18. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    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. Gunpinar, Serkan & Centeno, Grisselle, 2016. "An integer programming approach to the bloodmobile routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 94-115.
    2. Anna Nagurney & Pritha Dutta, 2019. "Supply chain network competition among blood service organizations: a Generalized Nash Equilibrium framework," Annals of Operations Research, Springer, vol. 275(2), pages 551-586, April.
    3. Dillon, Mary & Oliveira, Fabricio & Abbasi, Babak, 2017. "A two-stage stochastic programming model for inventory management in the blood supply chain," International Journal of Production Economics, Elsevier, vol. 187(C), pages 27-41.
    4. Dehghani, Maryam & Abbasi, Babak & Oliveira, Fabricio, 2021. "Proactive transshipment in the blood supply chain: A stochastic programming approach," Omega, Elsevier, vol. 98(C).
    5. Ramezanian, Reza & Behboodi, Zahra, 2017. "Blood supply chain network design under uncertainties in supply and demand considering social aspects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 69-82.
    6. Lowalekar, Harshal & Ravi, R. Raghavendra, 2017. "Revolutionizing blood bank inventory management using the TOC thinking process: An Indian case study," International Journal of Production Economics, Elsevier, vol. 186(C), pages 89-122.
    7. Ana Margarida Araújo & Daniel Santos & Inês Marques & Ana Barbosa-Povoa, 2020. "Blood supply chain: a two-stage approach for tactical and operational planning," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(4), pages 1023-1053, December.
    8. Bruno, Giuseppe & Diglio, Antonio & Piccolo, Carmela & Cannavacciuolo, Lorella, 2019. "Territorial reorganization of regional blood management systems: Evidences from an Italian case study," Omega, Elsevier, vol. 89(C), pages 54-70.
    9. Duan, Qinglin & Liao, T. Warren, 2014. "Optimization of blood supply chain with shortened shelf lives and ABO compatibility," International Journal of Production Economics, Elsevier, vol. 153(C), pages 113-129.
    10. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Cheraghi, Sara, 2020. "Robust and stable flexible blood supply chain network design under motivational initiatives," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    11. Hamdan, Bayan & Diabat, Ali, 2020. "Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    12. van Sambeeck, J.H.J. & van Brummelen, S.P.J. & van Dijk, N.M. & Janssen, M.P., 2022. "Optimal blood issuing by comprehensive matching," European Journal of Operational Research, Elsevier, vol. 296(1), pages 240-253.
    13. Wang, Ke-Ming & Ma, Zu-Jun, 2015. "Age-based policy for blood transshipment during blood shortage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 166-183.
    14. Janssen, Larissa & Claus, Thorsten & Sauer, Jürgen, 2016. "Literature review of deteriorating inventory models by key topics from 2012 to 2015," International Journal of Production Economics, Elsevier, vol. 182(C), pages 86-112.
    15. Mari Ito & Ryuta Takashima, 2023. "Evaluating Inventory Management Policies of Platelets at Regional-Block Blood Centers in Japan," SN Operations Research Forum, Springer, vol. 4(3), pages 1-22, September.
    16. Pahl, Julia & Voß, Stefan, 2014. "Integrating deterioration and lifetime constraints in production and supply chain planning: A survey," European Journal of Operational Research, Elsevier, vol. 238(3), pages 654-674.
    17. Anand Paul & Tharanga Rajapakshe & Suman Mallik, 2019. "Socially Optimal Contracting between a Regional Blood Bank and Hospitals," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 908-932, April.
    18. Turgay Ayer & Can Zhang & Chenxi Zeng & Chelsea C. White III & V. Roshan Joseph, 2019. "Analysis and Improvement of Blood Collection Operations," Service Science, INFORMS, vol. 21(1), pages 29-46, January.
    19. 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.
    20. Diabat, Ali & Jabbarzadeh, Armin & Khosrojerdi, Amir, 2019. "A perishable product supply chain network design problem with reliability and disruption considerations," International Journal of Production Economics, Elsevier, vol. 212(C), pages 125-138.

    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:annopr:v:335:y:2024:i:1:d:10.1007_s10479-023-05778-5. 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.