1.Nidhi, M.B., Chandran, A.G. and Pillai, V.M., 2013. Sustainability assessment of blood bag supply chain:
A case study. In 2013 IEEE Global Humanitarian Technology Conference: South Asia, pp.112-118.
https://doi.org/10.1109/GHTC-SAS.2013.6629899.
3. American Red Cross, 2014a. http://www.redcrossblood.com.
4. Gunpinar, S. and Centeno, G., 2015. Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers and Operations Research, 54, pp.129-141.
https://doi.org/10.1016/j.cor.2014.08.017.
5. Delen, D., Erraguntla, M., Mayer, R.J. and Wu, C.N., 2011. Better management of blood supply-chain with
GIS-based analytics. Annals of Operations Research, 185, pp.181-193. https://doi.org /10.1007/s10479-009-0616-2.
6. Larimi, N.G. and Yaghoubi, S., 2019. A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers and Industrial Engineering, 137, p.106014.
https://doi.org/10.1016/j.cie.2019.106014.
7. https:// www.IBTO.ir.
8. Pishvaee, M.S., Razmi, J. and Torabi, S.A., 2012. Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, pp.1-20.
https://doi.org/10.1016/j.fss.2012.04.010.
9. Riahi, N., Hosseini-Motlagh, S.M. and Teimourpour, B., 2013. A three-phase hybrid time's series modeling framework for improved hospital inventory demand forecast. International Journal of Hospital Research, 2(3), pp.133-142.
10. Roshan, M., Tavakkoli-Moghaddam, R. and Rahimi, Y., 2019. A two-stage approach to agile pharmaceutical supply chain management with product substitutability in crises. Computers and Chemical Engineering, 127, pp.200-217.
https://doi.org/10.1016/j.compchemeng.2019.05.014.
11. World Health Organization. Blood safety and availability. Acessed in January, 2021, from.
12. Torrado, A.S., and Barbosa-Povoa, A., 2022. Towards an optimized and sustainable blood supply chain network under uncertainty: A Literature Review. Cleaner Logistics and Supply Chain, 3, p.100028.
https://doi.org/10.1016/j.clscn.2022.100028.
13. 13. Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A.A. and Amin, S.H., 2018. Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Arti cial Intelligence, 71, pp.236-250.
https://doi.org/10.1016/j.engappai.2018.03.004.
14. Heidari-Fathian, H. and Pasandideh, S.H.R., 2018. Green-blood supply chain network design: Robust optimization, bounded objective function and Lagrangian relaxation. Computers and Industrial Engineering, 122,pp.95-105.
https://doi.org/10.1016/j.cie.2018.05.051.
15. Arani, M., Chan, Y., Liu, X. and Momenitabar, M., 2021. A lateral resupply blood supply chain network design under uncertainties. Applied Mathematical Modelling, 93, pp.165-187.
https://doi.org/10.1016/j.apm.2020.12.010.
16. Asadpour, M., Boyer, O. and Tavakkoli-Moghaddam, R., 2021. A blood supply chain network with
backup facilities considering blood groups and expiration date: A real-world. application. International Journal of Engineering, 34(2), pp.470-479.
https://doi.org/10.5829/IJE.2021.34.02B.19.
17. Eskandari, R. and Feili, H.R., 2021. Designing and solving location-routing-allocation problems in a sustainable
blood supply Chain network of blood transport in uncertainty conditions. International Journal of Innovation
in Management, Economics and Social Sciences, 1(4), pp.32-49.
https://doi.org/10.52547/ijimes.1.4.32.
18. Ghahremani-Nahr, J., Kian, R., Sabet, E. and Akbari, V., 2022. A bi-objective blood supply chain model under
uncertain donation, demand, capacity and cost: A robust possibilistic-necessity approach. Operational Research,
22(5), pp.4685-4723.https://doi.org/10.1007/s12351-022-00710-4.
19. Zarrinpoor, N., 2021. Designing a sustainable supply chain network for producing highvalue products from waste glass. Waste Management & Research, 39(12), pp.1489-1500.
https://doi.org/10.1177/0734242X21994669.
20. Tirkolaee, E. B., Golp^ra, H., Javanmardan, A. and Maihami, R., 2023. 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. Socio-Economic Planning Sciences, 85, p.101439.
https://doi.org/10.1016/j.seps.2022.101439.
21. Mohammadian-Behbahani, Z., Jabbarzadeh, A. and Pishvaee, M. S. 2021. A robust optimisation model
for sustainable blood supply chain network design under uncertainty. International Journal of Industrial and Systems Engineering, 31(4), pp.475-494.
https://doi.org/10.1504/IJISE.2019.099190.
22. Mousavi, R., Salehi-Amiri, A., Zahedi, A. and Hajiaghaei-Keshteli, M., 2021. Designing a supply chain network for blood decomposition by utilizing social and environmental factor. Computers and Industrial Engineering, 160, p.107501.
https://doi.org/10.1016/j.cie.2021.107501.
23. Van Zyl G.J.J., 1964. Inventorycontrol for perishable commodities. Dissertation, University of North Carolina.
24. Nahmias, S., 1982. Perishable inventory theory: A review. Operation Research, 30(4), pp.680-708.
https://doi.org/10.1287/opre.30.4.680.
26. Jabbarzadeh, A., Fahimnia, B. and 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, pp.225-244.
https://doi.org/10.1016/j.tre.2014.06.003.
27. Fereiduni, M. and Shahanaghi, K., 2016. A robust optimization model for blood supply chain in emergency situations. International Journal of Industrial Engineering Computations, 7(4), pp.535-554.
https://doi.org/10.5267/j.ijiec.2016.5.002.
28. Ramezanian, R. and 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,
pp.69-82.
https://doi.org/10.1016/j.tre.2017.06.004.
29. Osorio, A.F., Brailsford, S.C. and Smith, H.K., 2018. Whole blood or apheresis donations? A multiobjective stochastic optimization approach. European Journal of Operational Research, 266(1), pp.193-204.
https://doi.org/10.1016/j.ejor.2017.09.005.
30. Samani, M.R.G. and Hosseini-Motlagh, S.M., 2019. An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283(1), pp.1413-1462.
https://doi.org/10.1007/s10479-018-2873-4.
31. Diabat, A., Jabbarzadeh, A. and Khosrojerdi, A., 2019. A perishable product supply chain network design problem with reliability and disruption considerations.International Journal of Production Economics, 212, pp.125-13.
https://doi.org/10.1016/j.ijpe.2018.09.018.
32. Rajendran, S. and Ravindran, A.R., 2019. Inventory management of platelets along blood supply chain to minimize wastage and shortage. Computers & Industrial Engineering, 130, pp.714-730.
https://doi.org/10.1016/j.cie.2019.03.010.
33. Salehi, F., Mahootchi, M. and Husseini, S.M.M., 2019 Developing a robust stochastic model for designing a
blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research, 283(1-2), pp.679-703. https://doi.org/10.1007/s10479-017-2533-0.
34. Jemai, J., Do Chung, B. and Sarkar, B., 2020. Environmental e ect for a complex green supply-chain management to control waste: A sustainable approach. Journal of Cleaner Production, 277, p.122919.
https://doi.org/10.1016/j.jclepro.2020.122919.
35. Haghjoo, N., Tavakkoli-Moghaddam, R., ShahmoradiMoghadam, H. and Rahimi, Y., 2020. Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Arti cial Intelligence, 90, p.103493.
https://doi.org/10.1016/j.engappai.2020.103493.
36. 36. Hamdan, B., and Diabat, A., 2020. Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
https://doi.org/10.1016/j.tre.2019.08.005.
37. Hosseini-Motlagh, S.M., Samani, M.R.G. and 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), pp.1085-1104.
https://doi.org/10.1007/s12652-019-01315-0.
38. Ghahremani-Nahr, J., Nozari, H. and Bathaee, M., 2021. Robust box approach for blood supply Chain network design under uncertainty: Hybrid moth- ame optimization and genetic algorithm. International Journal of Innovation in Engineering, 1(2), pp.40-62.
https://doi.org/10.52547/ijie.1.2.40.
39. Shirazi, H., Kia, R. and Ghasemi, P., 2021. A stochastic bi-objective simulation-optimization model for plasma supply chain in case of COVID-19 outbreak. Applied Soft Computing, 112, p.107725.
https://doi.org/10.1016/j.asoc.2021.107725.
40. Eslamipoor, R. and Nobari, A., 2022. A reliable and sustainable design of supply chain in healthcare under uncertainty regarding environmental impacts. Journal of Applied Research on Industrial Engineering, 10(2), pp.256-272.
https://doi.org/10.22105/jarie.2022.335389.1461.
41. Khodaverdi, R., Shahbazi, M., Azar, A. and Fathi, M.R., 2022. A Robust Optimization Approach for Sustainable
humanitarian supply chain management of blood products. International Journal of Hospital Research, 11(1).
https://doi.org /IJHR.2021.291256.1487.
42. Baghbani, B., 2022. A mixed integer programming optimization of blood plasma supply Chain in the uncertainty conditions during COVID-19: A real sase in Iran. Discrete Dynamics in Nature and Society, 2022, p.3783119.
https://doi.org/10.1155/2022/3783119.
43. Kohneh, J.N., Teymoury, E. and Pishvaee, M.S., 2016. Blood products supply chain design considering disaster circumstances (Case study: Earthquake disaster in Tehran). Journal of Industrial and Systems Engineering, 9(special issue on supply chain), pp.51-72.
44. Salehi, F., Allahyari Emamzadeh, Y., Mirzapour, A.E.,Hashem, S.M.J. and Sha ei Aghdam, R., 2021. An L-shaped method to solve a stochastic blood supplychain network design problem in a natural disaster. Advances in Industrial Engineering, 55(1), pp.47-68.
https://doi.org/10.22059/jieng.2021.325375.1776.
45. Oberkampf, W.L., DeLand, S.M., Rutherford, B.M.,Diegert, K.V. and Alvin, K.F., 2002. Error and uncertainty in modeling and simulation. Reliability Engineering and System Safety, 75(3), pp.333-357.
https://doi.org/10.1016/S0951-8320(01)00120-X.
46. Zarrinpoor, N. and Pishvaee, M.S., 2021. Designing a municipal solid waste management system under disruptions using an enhanced L-shaped method. Journal of Cleaner Production, 299, p.126672.
https://doi.org/10.1016/j.jclepro.2021.126672.
47. Pishvaee, M.S., Rabbani, M. and Torabi, S.A., 2011. A robust optimization approach to closedloop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), pp.637-649.
https://doi.org/10.1016/j.apm.2010.07.013.
49. Inuiguchi, M. and Ramik, J., 2000. Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), pp.3-28. https://doi.org/10.1016/S0165-0114(98)00449-7.
50. TTalaei, M., Moghaddam, B.F., Pishvaee, M.S.,Bozorgi-Amiri, A. and Gholamnejad, S., 2016. A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 113, pp.662-673.
https://doi.org/10.1016/j.jclepro.2015.10.074.
51. Torabi, S.A. and Hassini, E., 2008. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), pp.193-214.
https://doi.org/10.1016/j.fss.2007.08.010.
55. Selim, H. and Ozkarahan, I., 2008. A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3), pp.401-418. https://doi.org/10.1007/s00170-006-0842-6.