Open Access
RAIRO-Oper. Res.
Volume 57, Number 1, January-February 2023
Page(s) 59 - 85
Published online 12 January 2023
  • S. Nahmias, Perishable inventory theory: a review. Oper. Res. 30 (1982) 680–708. [Google Scholar]
  • M. Habibi-Kouchaksaraei, M.M. Paydar and E. Asadi-Gangraj, Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Appl. Math. Modell. 55 (2018) 583–599. [CrossRef] [Google Scholar]
  • T.M. Whitin, Inventory control and price theory. Manage. Sci. 2 (1955) 61–68. [Google Scholar]
  • R.C. Elston and J.C. Pickrel, A statistical approach to ordering and usage policies for a hospital blood bank. Transfusion 3 (1963) 41–47. [CrossRef] [Google Scholar]
  • R.C. Elston and J.C. Pickrel, Guides to inventory levels for a hospital blood bank determined by electronic computer simulation. Transfusion 5 (1965) 465–470. [CrossRef] [PubMed] [Google Scholar]
  • G.P. Prastacos, Blood inventory management: an overview of theory and practice. Manage. Sci. 30 (1984) 777–800. [CrossRef] [Google Scholar]
  • W.P. Pierskalla, Supply chain management of blood banks, in Operations Research and Health Care.Springer, MA, Boston, 2005, pp. 103–145. [CrossRef] [Google Scholar]
  • H.K. Rajagopalan, C. Saydam and J. Xiao, A multiperiod set covering location model for dynamic redeployment of ambulances. Comput. Oper. Res. 35 (2008) 814–826. [CrossRef] [Google Scholar]
  • J.C. Papageorgiou, Some operations research applications to problems of health care systems (a survey). Int. J. Bio-Med. Comput. 9 (1978) 101–114. [CrossRef] [Google Scholar]
  • A. Rais and A. Viana, Operations research in healthcare: a survey. Int. Trans. Oper. Res. 18 (2011) 1–31. [Google Scholar]
  • S.S. Syam and M.J. Côé, A location–allocation model for service providers with application to not-for-profit health care organizations. Omega 38 (2010) 157–166. [CrossRef] [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]
  • A. Nagurney, A.H. Masoumi and M. Yu, Supply chain network operations management of a blood banking system with cost and risk minimization. Comput. Manage. Sci. 9 (2012) 205–231. [CrossRef] [Google Scholar]
  • Y. Sha and J. Huang, The multi-period location–allocation problem of engineering emergency blood supply systems. Syst. Eng. Proc. 5 (2012) 21–28. [CrossRef] [Google Scholar]
  • Q. Duan and T.W. Liao, Optimization of blood supply chain with shortened shelf lives and ABO compatibility. Int. J. Prod. Econ. 153 (2014) 113–129. [CrossRef] [Google Scholar]
  • M. Arvan, R. Tavakkoli-Moghaddam and M. Abdollahi, Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Manage. 3 (2015) 57–68. [CrossRef] [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]
  • J. Beliën, L. De Boeck, J. Colpaert, S. Devesse and F. Van den Bossche, Optimizing the facility location design of organ transplant centers, Decis. Support Syst. 54 (2013) 1568–1579. [CrossRef] [Google Scholar]
  • M.R.G. Samani and S.-M. Hosseini-Motlagh, An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Ann. Oper. Res. 283 (2019) 1413–1462. [CrossRef] [MathSciNet] [Google Scholar]
  • Z. Hosseinifard and B. Abbasi, The inventory centralization impacts on sustainability of the blood supply chain. Comput. Oper. Res. 89 (2018) 206–212. [CrossRef] [MathSciNet] [Google Scholar]
  • N. Yates, S. Stanger, R. Wilding and S. Cotton, Approaches to assessing and minimizing blood wastage in the hospital and blood supply chain. ISBT Sci. Ser. 12 (2017) 91–98. [CrossRef] [Google Scholar]
  • A.F. Osorio, S.C. Brailsford, H.K. Smith and J. Blake, Designing the blood supply chain: how much, how and where?. Vox Sanguinis 113 (2018) 760–769. [CrossRef] [PubMed] [Google Scholar]
  • M. Dehghani, B. Abbasi and F. Oliveira, Proactive transshipment in the blood supply chain: a stochastic programming approach. Omega 98 (2021) 102112. [CrossRef] [Google Scholar]
  • S.-M. Hosseini-Motlagh, M.R. Ghatreh Samani and S. Homaei, Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). J. Ambient Intell. Human. Comput. 11 (2020) 1085–1104. [CrossRef] [Google Scholar]
  • N. Haghjoo, R. Tavakkoli-Moghaddam, H. Shahmoradi-Moghadam and Y. Rahimi, Reliable blood supply chain network design with facility disruption: a real-world application. Eng. App. Artif. Intell. 90 (2020) 103493. [CrossRef] [Google Scholar]
  • H. Sun, Y. Wang, Y. Xue, A bi-objective robust optimization model for disaster response planning under uncertainties. Comput. Ind. Eng. 155 (2021) 107213. [CrossRef] [Google Scholar]
  • S. Rajendran and A. Ravi Ravindran, Inventory management of platelets along blood supply chain to minimize wastage and shortage. Comput. Ind. Eng. 130 (2019) 714–730. [CrossRef] [Google Scholar]
  • F. Salehi, M. Mahootchi and S.M. Moattar Husseini, Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran. Ann. Oper. Res. 283 (2019) 679–703. [CrossRef] [MathSciNet] [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]
  • B. Zahiri and M.S. Pishvaee, Blood supply chain network design considering blood group compatibility under uncertainty. Int. J. Prod. Res. 55 (2017) 2013–2033. [CrossRef] [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]
  • U. Abdulwahab and M.I.M. Wahab, Approximate dynamic programming modeling for a typical blood platelet bank. Comput. Ind. Eng. 78 (2014) 259–270. [CrossRef] [Google Scholar]
  • S.C. Das, A.M. Zidan, A.K. Manna, A.A. Shaikh and A.K. Bhunia, An application of preservation technology in inventory control system with price dependent demand and partial backlogging. Alexandria Eng. J. 59 (2020) 1359–1369. [CrossRef] [Google Scholar]
  • C.W. Kang, M. Imran, M. Omair, W. Ahmed, M. Ullah and B. Sarkar, Stochastic-petri net modeling and optimization for outdoor patients in building sustainable healthcare system considering staff absenteeism. Mathematics 7 (2019) 499. [CrossRef] [Google Scholar]
  • S.K. Sardar, B. Sarkar and B. Kim, Integrating machine learning, radio frequency identification, and consignment policy for reducing unreliability in smart supply chain management. Processes 9 (2021) 247. [CrossRef] [Google Scholar]
  • F. Behroozi, M.A.S. Monfared and S.M.H. Hosseini, Investigating the conflicts between different stakeholders’ preferences in a blood supply chain at emergencies: a trade-off between six objectives. Soft Comput. 25 (2021) 13389–13410. [CrossRef] [Google Scholar]
  • K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18 (2013) 577–601. [Google Scholar]
  • R. Ramezanian and Z. Behboodi, Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transp. Res. Part E: Logistics Transp. Rev. 104 (2017) 69–82. [CrossRef] [Google Scholar]
  • A. Jabbarzadeh, B. Fahimnia and S. Seuring, Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transp. Res. Part E: Logistics Transp. Rev. 70 (2014) 225–244. [Google Scholar]
  • S. Khalilpourazari and A.A. 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]
  • F. Behroozi, S.M.H. Hosseini and S.S. Sana, Teaching–learning-based genetic algorithm (TLBGA) an improved solution method for continuous optimization problems. Int. J. Syst. Assur. Eng. Manage. 12 (2021) 1362–1384. [CrossRef] [Google Scholar]
  • A. Hassani, S.M.H. Hosseini and F. Behroozi, Minimizing the operational costs in a flexible flow shop scheduling problem with unrelated parallel machines. J. Optim. Ind. Eng. 14 (2021) 169–184. [Google Scholar]
  • H. Iba and C.C. Aranha, Introduction to genetic algorithms, in Practical Applications of Evolutionary Computation to Financial Engineering. Springer, Berlin, Heidelberg (2012) 1–17. [Google Scholar]
  • D.A. Van Veldhuizen and G.B. Lamont, Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8 (2000) 125–147. [CrossRef] [PubMed] [Google Scholar]
  • C.A.C. Coello and Nareli Cruz Cortés, Solving multiobjective optimization problems using an artificial immune system. Genet. Program. Evol. Mach. 6 (2005) 163–190. [CrossRef] [Google Scholar]
  • A. Britto and A. Pozo, Using reference points to update the archive of MOPSO algorithms in many-objective optimization. Neurocomputing 127 (2014) 78–87. [CrossRef] [Google Scholar]
  • C.A.C. Coello, G.B. Lamont and D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems. Vol. 5. Springer, New York (2007). [Google Scholar]
  • V. Hajipour, P. Fattahi, M. Tavana and D. Di Caprio, Multi-objective multi-layer congested facility location–allocation problem optimization with Pareto-based meta-heuristics. Appl. Math. Modell. 40 (2016) 4948–4969. [CrossRef] [Google Scholar]
  • C.-L. Hsieh, An evolutionary-based optimization for a multi-objective blood banking supply chain model, in International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, Springer, Cham (2014) 511–520. [Google Scholar]
  • M. Asadpour, O. Boyer and R. Tavakkoli-Moghaddam, A blood supply chain network with backup facilities considering blood groups and expiration date: a real-world application. Int. J. Eng. 34 (2021) 470–479. [Google Scholar]

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