[1] Osorio, A. F., Brailsford, S. C., Smith, H. K., Forero-Matiz, S. P., & Camacho-Rodríguez, B. A. (2017). Simulation-optimization model for production planning in the blood supply chain. Health care management science, 20(4), 548–564. DOI:10.1007/s10729-016-9370-6
[2] 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. DOI:10.1016/j.ijpe.2017.02.006
[3] 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. DOI:10.1080/00207543.2016.1262563
[4] Zahiri, B., Torabi, S. A., Mohammadi, M., & Aghabegloo, M. (2018). A multi-stage stochastic programming approach for blood supply chain planning. Computers and industrial engineering, 122, 1–14. DOI:10.1016/j.cie.2018.05.041
[5] Kalantari, M., & Pishvaee, M. S. (2016). A robust possibilistic programming approach to drug supply chain master planning. Journal of industrial engineering research in production systems, 4(7), 49-67. (In Persian). https://ier.basu.ac.ir/article_1568.html?lang=en
[6] Nahmias, S. (1978). Fixed-charge perishable inventory problem. Operations research, 26(3), 464–481. DOI:10.1287/opre.26.3.464
[7] Ghandforoush, P., & Sen, T. K. (2010). A DSS to manage platelet production supply chain for regional blood centers. Decision support systems, 50(1), 32–42. DOI:10.1016/j.dss.2010.06.005
[8] Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Savelsbergh, M. W. P. (2010). Vendor managed inventory for environments with stochastic product usage. European journal of operational research, 202(3), 686–695. DOI:10.1016/j.ejor.2009.06.003
[9] Nagurney, A., & Masoumi, A. H. (2012). Supply chain network design of a sustainable blood banking system. International series in operations research and management science, 174, 49–72. DOI:10.1007/978-1-4419-6105-1_5
[10] Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers and operations research, 54, 129–141. DOI:10.1016/j.cor.2014.08.017
[11] Ramezanian, R., & Behboodi, Z. (2017). Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation research part E: logistics and transportation review, 104, 69–82. DOI:10.1016/j.tre.2017.06.004
[12] Najafi, M., Ahmadi, A., & Zolfagharinia, H. (2017). Blood inventory management in hospitals: considering supply and demand uncertainty and blood transshipment possibility. Operations research for health care, 15, 43–56. DOI:10.1016/j.orhc.2017.08.006
[13] Ghatreh Samani, M. R., Torabi, S. A., & Hosseini-Motlagh, S. M. (2018). Integrated blood supply chain planning for disaster relief. International journal of disaster risk reduction, 27, 168–188. DOI:10.1016/j.ijdrr.2017.10.005
[14] Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers and operations research, 101, 130–143. DOI:10.1016/j.cor.2018.09.001
[15] Hosseini-Motlagh, S. M., Samani, M. R. G., & Homaei, S. (2020). Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of ambient intelligence and humanized computing, 11(3), 1085–1104. DOI:10.1007/s12652-019-01315-0
[16] Derikvand, H., Hajimolana, S. M., Jabbarzadeh, A., & Najafi, S. E. (2020). A robust stochastic bi-objective model for blood inventory-distribution management in a blood supply chain. European journal of industrial engineering, 14(3), 369–403. DOI:10.1504/EJIE.2020.107676
[17] Doodman, M., & Bozorgi Amiri, A. (2019). Integrate blood supply chain network design with considering lateral transshipment under uncertainty. Journal of industrial management perspective, 9(3), 9–40. (In Persian). https://www.sid.ir/paper/408439/en
[18] Arvan, M., Tavakkoli-Moghaddam, R., & Abdollahi, M. (2015). Designing a bi-objective, multi-product supply chain network for blood supply. Uncertain supply chain management, 3(1), 57–68. DOI:10.5267/j.uscm.2014.8.004
[19] Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: robust optimization, bounded objective function & Lagrangian relaxation. Computers and industrial engineering, 122, 95–105. DOI:10.1016/j.cie.2018.05.051
[20] Larimi, N. G., & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & industrial engineering, 137, 106014.
[21] Kamyabniya, A., Lotfi, M. M., Naderpour, M., & Yih, Y. (2018). Robust platelet logistics planning in disaster relief operations under uncertainty: a coordinated approach. Information systems frontiers, 20(4), 759–782. DOI:10.1007/s10796-017-9788-5
[22] Eskandari, Z., Avakh Darestani, S., Imannezhad, R., & Sharifi, M. (2021). Optimizing a fuzzy multi-objective closed-loop supply chain model considering financial resources using meta-heuristic. Scientia Iranica, 30(4), 1480–1497.
[23] Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy sets and systems, 206, 1–20.
[24] Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: a fuzzy mathematical programming approach. Fuzzy sets and systems, 157(1), 74–97.
[25] Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143–161.
[26] Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European journal of operational research, 177(3), 1599–1609.
[27] Pishvaee, M. S., & Khalaf, M. F. (2016). Novel robust fuzzy mathematical programming methods. Applied mathematical modelling, 40(1), 407–418.