A scientometric review of the blood supply chain literature (2010-2025): Evolution, trends, and intellectual structure

Ramezanian, R.

Growing Science » Scientometrica » A scientometric review of the blood supply chain literature (2010-2025): Evolution, trends, and intellectual structure

Journals

SCI Volumes

Volume 1 (21)
- Issue 1 (6)
- Issue 2 (5)
- Issue 3 (5)
- Issue 4 (5)

Volume 2 (5)
- Issue 1 (5)

Countries

Scientometrica

ISSN 3115-8455 (Online) - ISSN 3115-8447 (Print) Quarterly Publication Volume 1 Issue 1 pp. 27-34 , 2025

A scientometric review of the blood supply chain literature (2010-2025): Evolution, trends, and intellectual structure Pages 27-34 Download PDF

Authors: Reza Ramezanian

DOI: 10.5267/j.sci.2025.1.004

Keywords: Blood supply chain, Scientometric review, Robust optimization, Fuzzy programming, Metaheuristics, Healthcare logistics, Resilience, Sustainability

Abstract: The blood supply chain (BSC) is a crucial and intricate system in the healthcare sector, which is marked by perishable products, fluctuating supply and demand, and a major impact of inefficiency. This paper showcases a detailed scientific review of BSC literature from 2010 to 2025 through scientometric methods, thereby mapping out its intellectual structure and development. By scrutinizing both foundational and recent publications, the authors are able to point out the research streams, methodological trends and main scholars. The scrutiny brings forward three leading research paradigms: (1) robust and resilient network design for disaster response, with Jawad as the leading scholar; (2) green and sustainable BSC modeling under uncertainty, where Pishvaee and his team are the main contributors; and (3) integrated inventory-routing problems for perishables, with Ramezanian as the pivotal author. This discipline is moving away from deterministic, single-objective models to the development of intricate multi-objective frameworks under hybrid uncertainties (robust, fuzzy, stochastic) which are being solved increasingly with metaheuristics and supported by case studies from real applications. The new trends include the combination of AI/ML for forecasting and decision-making, blockchain for transparency, and drones for the delivery part. The present review collects all these advancements and gives a succinct direction for both researchers and practitioners.

How to cite this paper

 Ramezanian, R. (2025). A scientometric review of the blood supply chain literature (2010-2025): Evolution, trends, and intellectual structure.Scientometrica, 1(1), 27-34.

Refrences

Abolghasemi, M., Abbasi, B., & Hosseinifard, Z. (2025). Machine learning for satisficing operational decision making: A case study in blood supply chain. International Journal of Forecasting, 41(1), 3–19. Abdolazimi, O., Pishvaee, M. S., Shafiee, M., Shishebori, D., Ma, J., & Entezari, S. (2025). Blood supply chain configu-ration and optimization under the COVID-19 using benders decomposition based heuristic algorithm. International Journal of Production Research, 63(2), 571–593. Agac, G., Baki, B., & Ar, I. M. (2024). Blood supply chain network design: a systematic review of literature and impli-cations for future research. Journal of Modelling in Management, 19(1), 68–118. Ahmadchali, M. A., Ebrahimzadeh-Afrouzi, M., Javadian, N., & Mahdavi, I. (2024). A robust location-allocation model for optimizing a multi-echelon blood supply chain network under uncertainty. OPSEARCH. Ahmadimanesh, M., Safabakhsh, H. R., & Sadeghi, S. (2023). Designing an optimal model of blood logistics manage-ment with the possibility of return in the three-level blood transfusion network. BMC Health Services Research, 23(1), 1304. Ailane, A., Bourekkache, S., Hamani, N., Bamoumen, M., & Kahoul, L. (2025). Blockchain-enabled sustainable blood supply network design. Operational Research, 25(3), 88. Ala, A., Simic, V., Bacanin, N., & Tirkolaee, E. B. (2024). Blood supply chain network design with lateral freight: A ro-bust possibilistic optimization model. Engineering Applications of Artificial Intelligence, 133, 108053. Alikhani, T., Dezfoulian, H., & Samouei, P. (2024). Blood supply chain location-inventory problem considering incen-tive programs: comparison and analysis of NSGA-II, NRGA and electromagnetic algorithms. Neural Computing and Applications, 36(31), 19469–19487. Altunoglu, B., & Batur Sir, G. D. (2024). Multi-objective location-distribution optimization in blood supply chain: an application in Turkiye. BMC Public Health, 24(1), 3181. Arvan, M., Tavakkoli-Moghaddam, R., & Abdollahi, M. (2015). Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management, 3(1), 57-68. Baghlani, A., Maki, S. N., & Jabbarzadeh, A. (2014). A robust optimization model for blood supply chain network de-sign. International Journal of Industrial Engineering Computations, 5(3), 381-400. Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16. Blake, J. T., Krok, E., Pavenski, K., Pambrun, C., & Petraszko, T. (2023). The operational impact of introducing cold stored platelets. Transfusion, 63(12), 2248–2255. Casucci, S., Walteros, J. L., & Bhandawat, R. (2024). A two-stage stochastic programming framework for blood product inventory management with ABO substitution and lateral transshipment. IISE Transactions on Healthcare Systems Engineering, 14(4), 362–383. Cheraghi, S., & Hosseini-Motlagh, S. M. (2017). A robust optimization model for blood supply chain network de-sign. International Journal of Industrial Engineering Computations, 8(3), 301-320. Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27-41. Diglio, A., Mancuso, A., Masone, A., & Sterle, C. (2024). Multi-echelon facility location models for the reorganization of the Blood Supply Chain at regional scale. Transportation Research Part E: Logistics and Transportation Review, 183, 103438. Duan, Q., & Liao, T. W. (2013). A new age-based replenishment policy for supply chain inventory optimization of high-ly perishable products. International Journal of Production Economics, 145(2), 658-671. Erdem, M., & Ozdemir, A. (2025). A novel two-echelon sustainable network design and optimization under a fuzzy en-vironment: a healthcare case study. Engineering Optimization. Esfandabadi, A. M., Shishebori, D., Fakhrzad, M.-B., & Khademi Zare, H. K. (2024). Developing a multi-objective model for a multi-level supply chain of blood products under uncertainty and the global pandemic: a hybrid robust optimization approach. Discover Applied Sciences, 6(8), 410. Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700-709. Fariman, S. K., Danesh, K., Pourtalebiyan, M., Fakhri, Z., Motallebi, A., & Fozooni, A. (2024). A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach. Scientific Reports, 14(1), 9452. Feghhi, B., Sangari, M. S., Keramati, A., & Rouhani-Tazangi, M. R. (2025). Designing a robust multi-objective blood supply chain network with permanent and mobile facilities for disaster relief. International Journal of Logistics Sys-tems and Management, 50(3), 386–409. Feng, Z., Chi, X., Hu, B., Liu, L., Li, D., & Pang, S. (2025). Establishment and application of a red blood cell gene data-base in regular blood donors. Chinese Journal of Blood Transfusion, 38(8), 1056–1062. Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129-141. Hosseini, S. M., Nookabadi, A., & Iranpoor, M. (2025). Robust design of a multi-echelon dynamic blood supply chain network for disaster relief. Journal of Modelling in Management. Hosseini-Motlagh, S.-M., Samani, M. R. G., & Faraji, M. (2024). Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty. Computers and Industrial Engineering, 188, 109908. Hosseini-Motlagh, S.-M., Samani, M. R. G., & Kordhaghi, H. (2026). A possibilistic programming approach in an inte-grated fuzzy periodic review model and clustering strategy for optimizing platelet supply chain. Expert Systems with Applications, 298, 129539. Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225-244. Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2016). A robust optimization model for blood supply chain network design. International Journal of Production Economics, 183, 700-709. K N, M., & M, G. (2025). An optimization approach for blood supply chain management integrating drone delivery method. Discover Applied Sciences, 7(8), 912. Karamipour, M., & Agha Mohammad Ali Kermani, M. A. M. A. (2024). Presenting a mathematical model of blood sup-ply chain considering the efficiency of collection centers and development of metaheuristic algorithm in M/M/C/K queuing system. Cerebral Cortex, 34(2), bhae012. Kumar, A., Chatterjee, I., Pallavi, Sharma, K., & Thakur, M. (2024). Real-Time-Based Blood Wastage Management Us-ing IoT and Blockchain Technology. SN Computer Science, 5(3), 310. Lusiantoro, L., Mara, S. T. W., & Rifai, A. P. (2024). Towards net zero healthcare transport operations in Indonesia: A total cost of ownership approach. *Socio-Economic Planning Sciences, 95*, 101985. Mohamadi, N., Niaki, S. T. A., Taher, M., & Shavandi, A. (2024). An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management. Engineering Applications of Artificial Intelli-gence, 127, 107403. Motamedi, M., Mousavi, S. M., & Darvish Motevalli, M. H. (2024). Designing a robust blood supply chain model under conditions of uncertainty in demand. Journal of Optimization in Industrial Engineering, 17(2), 215–228. Nagurney, A., Masoumi, A. H., & Yu, M. (2012). Supply chain network operations management of a blood banking sys-tem with cost and risk minimization. Computational Management Science, 9(2), 205-231. Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2017). A structured review of quantitative models for the blood supply chain. International Journal of Production Research, 55(24), 7196-7212. Pierskalla, W. P. (2005). Supply chain management of blood banks. In M. L. Brandeau, F. Sainfort, & W. P. Pierskalla (Eds.), Operations research and health care: A handbook of methods and applications (pp. 103-145). Springer. Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathe-matical programming. Applied Mathematical Modelling, 36(8), 3433-3446. Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain net-work design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, 67, 14-38. Qing, L., Yin, Y., Wang, D., Yu, Y., & Cheng, T. C. E. (2025). A two-stage adaptive robust model for designing a relia-ble blood supply chain network with disruption considerations in disaster situations. Naval Research Logistics, 72(1), 45–71. Ramezanian, R., & Behboodi, M. H. (2017). Blood supply chain network design under uncertainties in supply and de-mand considering social aspects. Transportation Research Part E: logistics and Transportation Review, 104, 69-82. Ramezanian, R., Saidi-Mehrabad, M., & Teimoury, E. (2013). A mathematical model for integrated production-distribution planning in a multi-period multi-product supply chain. International Journal of Advanced Manufactur-ing Technology, 67(5-8), 1677-1692. Samani, M. R. G., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2017). A two-stage stochastic programming model for blood supply chain network design with lateral transshipment. Journal of Industrial and Systems Engineering, 10(4), 1-20. Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply sys-tems. Systems Engineering Procedia, 5, 21-28. Sheibani, M., Ostovari, A., & Benyoucef, L. (2025). Multi-Objective Blood Supply Chain Network Design Under Uncer-tainty: Integrating Environmental and Social Considerations. Process Integration and Optimization for Sustainabil-ity, 9(2), 625–650. Viennet, E., Dean, M. M., Kircher, J., Leder, K., Guo, Y., Jones, P., & Faddy, H. M. (2025). Blood under pressure: how climate change threatens blood safety and supply chains. The Lancet Planetary Health, 9(4), e304–e313. Wang, C., & Chen, X. (2025). Robust optimization on disaster emergency blood supply chain based on air-ground transport. Journal of Highway and Transportation Research and Development, 42(6), 212–222. Yang, H., Yin, Y., Wang, D., Cheng, T. C. E., Zhang, R., & Hu, H. (2025). An integrated blood supply chain network de-sign during a pandemic. International Journal of Production Research, 63(9), 3384–3409. Zahedi, A., Salehi-Amiri, A., & Hajiaghaei-Keshteli, M. (2021). A sustainable closed-loop blood supply chain network design under uncertainty. Environmental Science and Pollution Research, 28(42), 59447-59481. Zahiri, B., & Pishvaee, M. S. (2017). Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research, 55(7), 2013-2033. Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2014). A robust possibilistic programming approach to multi-period location-allocation of organ transplant centers under uncertainty. Computers & Industrial Engineering, 74, 139-148.