IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v8y2024i4p134-d1550115.html
   My bibliography  Save this article

An Integrated Supply Chain Model for Predicting Demand and Supply and Optimizing Blood Distribution

Author

Listed:
  • Pooria Bagher Niakan

    (Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen 3973188981, Iran)

  • Mehdi Keramatpour

    (Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen 3973188981, Iran)

  • Behrouz Afshar-Nadjafi

    (Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin 3419915195, Iran)

  • Alireza Rashidi Komijan

    (Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran)

Abstract

Background : The blood supply chain (BSC) is crucial for providing safe and sufficient blood, but it faces numerous challenges and needs to be robust and resilient. This study provides a comprehensive model for managing and optimizing the BSC in real-world scenarios, including emergency and routine circumstances and with consideration of health equity concepts. Method : Classic time-series models are applied to predict future supply chain circumstances, addressing uncertainty in blood demand and the need for timely supply. A structured framework and medical preferences are prioritized to optimize distribution, minimize blood shortages, minimize wastage due to expiry, and maximize blood freshness. Genetic algorithms (GA) and particle swarm optimization (PSO) are used to solve mathematical models quickly and efficiently, ensuring reliable operation. Result : The model’s outcomes can effectively meet the daily needs of the BSC and assist decision-makers managing blood inventory and distribution, improving robustness and resilience. Conclusions : Utilizing weights allows for the effective management of each objective function to convert the model into a single-objective mixed-integer linear programming (SO-MILP) based on unique conditions, enabling the system to self-adjust for optimal performance, boosting the sustainability of the blood supply chain, and promoting the principle of health equity under diverse real-world settings.

Suggested Citation

  • Pooria Bagher Niakan & Mehdi Keramatpour & Behrouz Afshar-Nadjafi & Alireza Rashidi Komijan, 2024. "An Integrated Supply Chain Model for Predicting Demand and Supply and Optimizing Blood Distribution," Logistics, MDPI, vol. 8(4), pages 1-36, December.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:134-:d:1550115
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/8/4/134/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/8/4/134/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Civelek, Ismail & Karaesmen, Itir & Scheller-Wolf, Alan, 2015. "Blood platelet inventory management with protection levels," European Journal of Operational Research, Elsevier, vol. 243(3), pages 826-838.
    2. S. Dharmaraja & Srijan Narang & Vidyottama Jain, 2020. "A mathematical model for supply chain management of blood banks in India," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 541-552, June.
    3. Andres F. Osorio & Sally C. Brailsford & Honora K. Smith & Sonia P. Forero-Matiz & Bernardo A. Camacho-Rodríguez, 2017. "Simulation-optimization model for production planning in the blood supply chain," Health Care Management Science, Springer, vol. 20(4), pages 548-564, December.
    4. 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.
    5. Ensafian, Hamidreza & Yaghoubi, Saeed, 2017. "Robust optimization model for integrated procurement, production and distribution in platelet supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 32-55.
    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. Samani, Mohammad Reza Ghatreh & Hosseini-Motlagh, Seyyed-Mahdi & Homaei, Shamim, 2020. "A reactive phase against disruptions for designing a proactive platelet supply network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    2. Kamyabniya, Afshin & Noormohammadzadeh, Zohre & Sauré, Antoine & Patrick, Jonathan, 2021. "A robust integrated logistics model for age-based multi-group platelets in disaster relief operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    3. Andreea Avramescu & Richard Allmendinger & Manuel L'opez-Ib'a~nez, 2021. "Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models," Papers 2105.12699, arXiv.org.
    4. Gorria, Carlos & Lezaun, Mikel & López, F. Javier, 2022. "Performance measures of nonstationary inventory models for perishable products under the EWA policy," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1137-1150.
    5. Meneses, Maria & Santos, Daniel & Barbosa-Póvoa, Ana, 2023. "Modelling the Blood Supply Chain," European Journal of Operational Research, Elsevier, vol. 307(2), pages 499-518.
    6. Yuan Xu & Joseph Szmerekovsky, 2022. "A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty," Health Care Management Science, Springer, vol. 25(3), pages 441-459, September.
    7. Asadpour, Milad & Olsen, Tava Lennon & Boyer, Omid, 2022. "An updated review on blood supply chain quantitative models: A disaster perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    8. Jihed Jemai & Biswajit Sarkar, 2019. "Optimum Design of a Transportation Scheme for Healthcare Supply Chain Management: The Effect of Energy Consumption," Energies, MDPI, vol. 12(14), pages 1-27, July.
    9. 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.
    10. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    11. 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.
    12. Yavari, Mohammad & Bohreghi, Iman Mohammadi, 2025. "Developing a green-resilient power network and supply chain: Integrating renewable and traditional energy sources in the face of disruptions," Applied Energy, Elsevier, vol. 377(PC).
    13. Chua, Geoffrey A. & Senga, Juan Ramon L., 2022. "Blood supply interventions during disasters: Efficiency measures and strategies to mitigate volatility," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    14. 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.
    15. 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.
    16. Zhou, Quan Spring & Olsen, Tava Lennon, 2017. "Inventory rotation of medical supplies for emergency response," European Journal of Operational Research, Elsevier, vol. 257(3), pages 810-821.
    17. 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.
    18. Fabián Silva-Aravena & Irlanda Ceballos-Fuentealba & Eduardo Álvarez-Miranda, 2020. "Inventory Management at a Chilean Hospital Pharmacy: Case Study of a Dynamic Decision-Aid Tool," Mathematics, MDPI, vol. 8(11), pages 1-20, November.
    19. Masoumeh Vali & Khodakaram Salimifard & Amir H. Gandomi & Thierry J. Chaussalet, 2022. "Care process optimization in a cardiovascular hospital: an integration of simulation–optimization and data mining," Annals of Operations Research, Springer, vol. 318(1), pages 685-712, November.
    20. Kenan, Nabil & Diabat, Ali, 2022. "The supply chain of blood products in the wake of the COVID-19 pandemic: Appointment scheduling and other restrictions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).

    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:gam:jlogis:v:8:y:2024:i:4:p:134-:d:1550115. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.