Open Access
Issue
RAIRO-Oper. Res.
Volume 60, Number 1, January-February 2026
Page(s) 201 - 225
DOI https://doi.org/10.1051/ro/2025156
Published online 23 February 2026
  • T. Akita, J. Tanaka, M. Ohisa, A. Sugiyama, K. Nishida, S. Inoue and T.J.T. Shirasaka, Predicting future blood supply and demand in Japan with a Markov model: application to the sex-and age-specific probability of blood donation. Transfusion 56 (2016) 2750–2759. [Google Scholar]
  • A. Ala, A. Goli, S. Mirjalili and V. Simic, A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design. Appl. Soft Comput. 150 (2024) 111012. [CrossRef] [Google Scholar]
  • M. Arani, Y. Chan, X. Liu and M. Momenitabar, A lateral resupply blood supply chain network design under uncertainties. Appl. Math. Modell. 93 (2021) 165–187. [Google Scholar]
  • M. Arora and Y. Gigras, Importance of supply chain management in healthcare of third world countries. Int. J. Supply Oper. Manage. 5 (2018) 101–106. [Google Scholar]
  • B.J. Bain, Blood Cells: A Practical Guide. John Wiley and Sons (2014). [Google Scholar]
  • J. Beliën and H. Forcé, Supply chain management of blood products: a literature review. Eur. J. Oper. Res. 217 (2012) 1–16. [CrossRef] [Google Scholar]
  • V. Chvátal, Edmonds polytopes and a hierarchy of combinatorial problems. Discrete Math. 4 (1973) 305–337. [Google Scholar]
  • M. Dillon, F. Oliveira and B. Abbasi, A two-stage stochastic programming model for inventory management in the blood supply chain. Int. J. Prod. Econ. 187 (2017) 27–41. [CrossRef] [Google Scholar]
  • S. Edelkamp and S. Schrödl, Heuristic Search: Theory and Applications. Elsevier (2011). [Google Scholar]
  • S.A. Elhaj, Y. Odeh, D. Tbaishat, A. Rjoop, A. Mansour and M. Odeh, Informing the state of process modeling and automation of blood banking and transfusion services through a systematic mapping study. J. Multidisciplinary Healthcare 17 (2024) 473–489. [Google Scholar]
  • H. Ensafian, S. Yaghoubi and M.M. Yazdi, Raising quality and safety of platelet transfusion services in a patient-based integrated supply chain under uncertainty. Comput. Chem. Eng. 106 (2017) 355–372. [Google Scholar]
  • B. Fahimnia, A. Jabbarzadeh, A. Ghavamifar and M. Bell, Supply chain design for efficient and effective blood supply in disasters. Int. J. Prod. Econ., 183 (2017) 700–709. [CrossRef] [Google Scholar]
  • A. Fallahi, S.A. Mousavian Anaraki, H. Mokhtari and S.T.A. Niaki, Blood plasma supply chain planning to respond COVID-19 pandemic: a case study. Environ. Dev. Sustainability 26 (2024) 1965–2016. [Google Scholar]
  • B.E. Fan, K.H. Ong, S.S.W. Chan, B.E. Young, V.C.L. Chong, S.P.C. Chen, S.P. Lim, G.P. Lim and P. Kuperan, Blood and blood product use during COVID-19 infection. Am. J. Hematol. 95 (2020) E158. https://pmc.ncbi.nlm.nih.gov/articles/PMC7262362/. [Google Scholar]
  • S.K. Fariman, K. Danesh, M. Pourtalebiyan, Z. Fakhri, A. Motallebi and A. Fozooni, A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach. Scientific Reports 14 (2024) 9452. [Google Scholar]
  • M. Fazli-Khalaf, S. Khalilpourazari and M. Mohammadi, Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Ann. Oper. Res. 283 (2019) 1079–1109. [CrossRef] [MathSciNet] [Google Scholar]
  • N. Gholamian, I. Mahdavi, N. Mahdavi-Amiri and R. Tavakkoli-Moghaddam, Hybridization of an interactive fuzzy methodology with a lexicographic min–max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network. Comput. Ind. Eng. 158 (2021) 107282. [Google Scholar]
  • B.K. Giri and S.K. Roy, Neutrosophic multi-objective green four-dimensional fixed-charge transportation problem. Int. J. Mach. Learn. Cybern. 13 (2022) 3089–3112. [CrossRef] [Google Scholar]
  • B.K. Giri and S.K. Roy, CI-MM-Dombi operator based on interval type-2 spherical fuzzy set and its applications on sustainable supply chain with risk criteria: using CI-TODIM-MARCOS method. Soft Comput. 28 (2024) 10023–10056. [Google Scholar]
  • B.K. Giri and S.K. Roy, Fuzzy-random robust flexible programming on sustainable closed-loop renewable energy supply chain. Appl. Energy 363 (2024) 123044. [Google Scholar]
  • B.K. Giri, S.K. Roy and M. Deveci, Fuzzy robust flexible programming with Me measure for electric sustainable supply chain. Appl. Soft Comput. 145 (2023) 110614. [CrossRef] [Google Scholar]
  • B.K. Giri, S.K. Roy and M. Deveci, Projection based regret theory on three-way decision model in probabilistic interval-valued q-rung orthopair hesitant fuzzy set and its application to medicine company. Artif. Intell. Rev. 56 (2023) 3617–3649. [CrossRef] [Google Scholar]
  • R.E. Gomory, Outline of an algorithm for integer solutions to linear programs. Bull. Am. Math. Soc. 64 (1958) 275–278. [Google Scholar]
  • R.E. Gomory, Outline of an algorithm for integer solutions to linear programs and an algorithm for the mixed integer problem, in 50 Years of integer programming 1958–2008: From the early years to the State-of-the-Art. Springer Berlin Heidelberg, Berlin, Heidelberg (2009) 77–103. [Google Scholar]
  • S. Gunpinar and G. Centeno, Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Comput. Oper. Res. 54 (2015) 129–141. [Google Scholar]
  • X. Guo and X. Chen, The Impact of COVID-19 on the Blood Supply Chain: Effective Strategies to Avoid Blood Shortage, in Proceedings of the 7th International Conference on Industrial and Business Engineering (2021) 8–13. [Google Scholar]
  • R. Haijema, J. van der Wal and N.M. van Dijk, Blood platelet production: optimization by dynamic programming and simulation. Comput. Oper. Res. 34 (2007) 760–779. [Google Scholar]
  • S.M.H. Hosseini, F. Behroozi and S.S. Sana, Multi-objective optimization model for blood supply chain network design considering cost of shortage and substitution in disaster. RAIRO-Oper. Res. 57 (2023) 59–85. [Google Scholar]
  • S.M. Hosseini, A. Shahandeh Nookabadi and M. Iranpoor, Robust design of a multi-echelon dynamic blood supply chain network for disaster relief. J. Modell. Manage. 20 (2025) 1789–1822. [Google Scholar]
  • S.M. Hosseini-Motlagh, M.R.G. Samani and S. Homaei, Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). J. Ambient Intell. Humanized Comput. 11 (2020) 1085–1104. [Google Scholar]
  • C.Y.R. Huang, C.Y. Lai and K.T.T. Cheng, Fundamentals of algorithms, in Electronic Design Automation. Morgan Kaufmann (2009) 173–234. [Google Scholar]
  • M. Jahangoshai Rezaee, S. Yousefi and J. Hayati, A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy. J. Ind. Eng. Int. 13 (2017) 199–213. [Google Scholar]
  • S. Khalilpourazari and A. Arshadi Khamseh, Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Ann. Oper. Res. 283 (2019) 355–393. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Khalilpourazari and H. Hashemi Doulabi, A flexible robust model for blood supply chain network design problem. Ann. Oper. Res. 328 (2023) 701–726. [Google Scholar]
  • S. Khalilpourazari and H. Hashemi Doulabi, Robust modelling and prediction of the COVID-19 pandemic in Canada. Int. J. Prod. Res. 61 (2023) 8367–8383. [Google Scholar]
  • S. Khalilpourazari and M. Mohammadi, Optimization of closed-loop Supply chain network design: a Water Cycle Algorithm approach, in 2016 12th International Conference on Industrial Engineering (ICIE). IEEE (2016) 41–45. [Google Scholar]
  • S. Khalilpourazari, S. Soltanzadeh, G.W. Weber and S.K. Roy, Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study. Ann. Oper. Res. 289 (2020) 123–152. [Google Scholar]
  • S. Khalilpourazari, A. Mirzazadeh, G.W. Weber and S.H.R. Pasandideh, A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization (2020). DOI: 10.1080/02331934.2019.1630625. [Google Scholar]
  • K. Kungwalsong, A. Mendoza, V. Kamath, S. Pazhani, and J.A. Marmolejo-Saucedo, An application of interactive fuzzy optimization model for redesigning supply chain for resilience. Ann. Oper. Res. 315 (2022) 1803–1839. [Google Scholar]
  • A. Land and A. Doig, An automatic method of solving discrete programming problems. Ecometrics 28 (1960) 497–520. [Google Scholar]
  • A.H. Land and A.G. Doig, An Automatic Method for Solving Discrete Programming Problems. Springer Berlin Heidelberg (2010) 105–132. [Google Scholar]
  • A. Mansur, D.I. Handayani, I.D. Wangsa, D.M. Utama and W.A. Jauhari, A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types. Decis. Anal. J. 8 (2023) 100279. [Google Scholar]
  • A. Mondal, B.K. Giri and S.K. Roy, An integrated sustainable bio-fuel and bio-energy supply chain: a novel approach based on DEMATEL and fuzzy-random robust flexible programming with Me measure. Appl. Energy 343 (2023) 121225. [CrossRef] [Google Scholar]
  • A. Mondal, B.K. Giri, S.K. Roy, M. Deveci and D. Pamucar, Sustainable-resilient-responsive supply chain with demand prediction: an interval type-2 robust programming approach. Eng. Appl. Artif. Intell. 133 (2024) 108133. [CrossRef] [Google Scholar]
  • E.D.F.T.Q.O. Medicines, Guide to the preparation, use and quality assurance of blood components, in Recommendation No. R (95) 15. Manhattan Publishing Company (2013). [Google Scholar]
  • S. Mirjalili and A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95 (2016) 51–67. [CrossRef] [Google Scholar]
  • S. Moslemi and S.H.R. Pasandideh, A location–allocation model for quality-based blood supply chain under IER uncertainty. RAIRO-Oper. Res. 55 (2021) S967–S998. [Google Scholar]
  • A.K. Mukherjee, G. Maity, J. Jablonsky, S.K. Roy and G.W. Weber, A sustainable inventory optimisation considering imperfect production under uncertain environment. Int. J. Syst. Sci. Oper. Logistics 11 (2024) 2379540. [Google Scholar]
  • M. Najafi, A. Ahmadi and H. Zolfagharinia, Blood inventory management in hospitals: considering supply and demand uncertainty and blood transshipment possibility. Oper. Res. Health Care 15 (2017) 43–56. [Google Scholar]
  • Z. Nan, L. Zhimin, Q. Shen and L. Ting, An improved unordered pair bat algorithm for solving the symmetrical traveling salesman problem. Found. Comput. Decis. Sci. 47 (2022) 87–103. [Google Scholar]
  • A. Roshani, M.R. Gholamian and M. Arabi, Integrating the triple bottom line of sustainability, resilience strategies, and product perishability consideration to design a pharmaceutical supply chain network: a COVID-19 case study. RAIRO-Oper. Res. 58 (2024) 5121–5158. [Google Scholar]
  • G. S¸ahin, H. Süral and S. Meral, Locational analysis for regionalization of Turkish Red Crescent blood services. Comput. Oper. Res. 34 (2007) 692–704. [Google Scholar]
  • S.M. Sasser, R.C. Hunt, B. Bailey, J. Krohmer, S. Cantrill, K. Gerold, M. Johnson, A. Kellerman, P. Lenaghan, J. Morris and B. Myers, In a moment’s notice; surge capacity for terrorist bombings: challenges and proposed solutions (2007). [Google Scholar]
  • S.A. Seyfi-Shishavan, Y. Donyatalab, E. Farrokhizadeh and S.I. Satoglu, A fuzzy optimization model for designing an efficient blood supply chain network under uncertainty and disruption. Ann. Oper. Res. 331 (2023) 447–501. [Google Scholar]
  • A. Szmelter-Jarosz, J. Ghahremani-Nahr and H. Nozari, A neutrosophic fuzzy optimisation model for optimal sustainable closed-loop supply chain network during COVID-19. J. Risk Finan. Manage. 14 (2021) 519. [Google Scholar]
  • E.B. Tirkolaee, H. Golp^ıra, A. Javanmardan and R. Maihami, A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case study. Soc.-Econ. Planning Sci. 85 (2023) 101439. [CrossRef] [Google Scholar]
  • E. Uchoa, A. Pessoa and L. Moreno, Optimizing with Column Generation: Advanced Branch-Cut-and-Price Algorithms Part I. Vol. 2024. Universidade Federal Fluminense (2024). [Google Scholar]
  • Z. Yang, S. Wen, Q. Qi, X. Zhang, H. Shen, G. Chen, J. Xu, Z. Lv and A. Ji, Design of composite puncture blood collection system and research on puncture force. Comput. Methods Biomech. Biomed. Eng. 28 (2025) 1743–1754. [Google Scholar]
  • B. Zahiri and M.S. Pishvaee, Blood supply chain network design considering blood group compatibility under uncertainty. Int. J. Prod. Res. 55 (2016) 2013–2033. [Google Scholar]
  • B. Zahiri, S.A. Torabi, M. Mohammadi and M. Aghabegloo, A multi-stage stochastic programming approach for blood supply chain planning. Comput. Ind. Eng. 122 (2018) 1–14. [Google Scholar]
  • J. Zhang, C. Liu, X. Li, H.L. Zhen, M. Yuan, Y. Li and J. Yan, A survey for solving mixed integer programming via machine learning. Neurocomputing 519 (2023) 205–217. [Google Scholar]
  • D. Zhou, L.C. Leung and W.P. Pierskalla, Inventory management of platelets in hospitals: optimal inventory policy for perishable products with regular and optional expedited replenishments. Manuf. Serv. Oper. Manage. 13 (2011) 420–438. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.