Open Access
Issue
RAIRO-Oper. Res.
Volume 57, Number 4, July-August 2023
Page(s) 1995 - 2024
DOI https://doi.org/10.1051/ro/2023089
Published online 27 July 2023
  • A.N. Arnette and C.W. Zobel, A risk-based approach to improving disaster relief asset pre-positioning. Prod. Oper. Manag. 28 (2019) 457–478. [CrossRef] [Google Scholar]
  • H. Baharmand, T. Comes and M. Lauras, Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters. Transp. Res. Part E Logist. Transp. Rev. 127 (2019) 86–110. [CrossRef] [Google Scholar]
  • X. Bai, Two-stage multiobjective optimization for emergency supplies allocation problem under integrated uncertainty. Math. Prob. Eng. 2016 (2016) 1–13. [Google Scholar]
  • B. Balcik, S. Silvestri, M.É. Rancourt andG. Laporte, Collaborative prepositioning network design for regional disaster response. Prod. Oper. Manag. 28 (2019) 2431–2455. [CrossRef] [Google Scholar]
  • D. Briskorn, A. Kimms and D. Olschok, Simultaneous planning for disaster road clearance and distribution of relief goods: a basic model and an exact solution method. OR Spectr. 42 (2020) 591–619. [CrossRef] [Google Scholar]
  • J.-F. Camacho-Vallejo, E. González-Rodríguez, F.J. Almaguer and R.G. González-Ramírez, A bi-level optimization model for aid distribution after the occurrence of a disaster. J. Clean. Prod. 105 (2015) 134–145. [CrossRef] [Google Scholar]
  • C. Cao, C. Li, Q. Yang, Y. Liu and T. Qu, A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. J. Clean. Prod. 174 (2018) 1422–1435. [CrossRef] [Google Scholar]
  • C. Cao, Y. Liu, O. Tang and X. Gao, A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. Int. J. Prod. Econ. 235 (2021) 108081. [CrossRef] [Google Scholar]
  • C. Cao, J. Li, J. Liu, J. Liu, H. Qiu and J. Zhen, Sustainable development-oriented location-transportation integrated optimization problem regarding multi-period multi-type disaster medical waste during COVID-19 pandemic. Ann. Oper. Res. (2022) 1–47. [Google Scholar]
  • C. Cao, J. Liu, Y. Liu, H. Wang and M. Liu, Digital twin-driven robust bi-level optimisation model for COVID-19 medical waste location-transport under circular economy. Comput. Ind. Eng. (2023) 109107. [CrossRef] [Google Scholar]
  • C. Cao, Y. Xie, Y. Liu, J. Liu and F. Zhang, Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability. J. Clean. Prod. 389 (2023) 135985. [CrossRef] [Google Scholar]
  • CSSE, Center for Systems Science and Engineering, Confirmed COVID-19 Cases by Country/Region/Sovereignty, Johns Hopkins University. https://www.arcgis.com/apps/opsdashboard/index.html{#}/bda7594740fd40299423467b48e9ecf6 (Accessed 9-May-2020). [Google Scholar]
  • L.B. Davis, F. Samanlioglu, X. Qu and S. Root, Inventory planning and coordination in disaster relief efforts. Int. J. Prod. Econ. 141 (2013) 561–573. [CrossRef] [Google Scholar]
  • A. Döyen, N. Aras and G. Barbarosoğlu, A two-echelon stochastic facility location model for humanitarian relief logistics. Optim. Lett. 6 (2012) 1123–1145. [CrossRef] [MathSciNet] [Google Scholar]
  • O. Elci and N. Noyan, A chance-constrained two-stage stochastic programming model for humanitarian relief network design. Transp. Res. Part B Methodol. 108 (2018) 55–83. [CrossRef] [Google Scholar]
  • E.J. Emanuel, G. Persad, R. Upshur, B. Thome, M. Parker, A. Glickman, C. Zhang, C. Boyle, M. Smith and J.P. Phillips, Fair allocation of scarce medical resources in the time of Covid-19. N. Engl. J. Med. 382 (2020) 2049–2055. [CrossRef] [PubMed] [Google Scholar]
  • G. Erbeyoğlu and Ü. Bilge, A robust disaster preparedness model for effective and fair disaster response. Eur. J. Oper. Res. 280 (2020) 479–494. [CrossRef] [Google Scholar]
  • X. Gao, A bi-level stochastic optimization model for multi-commodity rebalancing under uncertainty in disaster response. Ann. Oper. Res. 319 (2022) 115–148. [CrossRef] [MathSciNet] [Google Scholar]
  • X. Gao, A location-driven approach for warehouse location problem. J. Oper. Res. Soc. 72 (2020) 2735–2754. [Google Scholar]
  • X. Gao and C. Cao, Multi-commodity rebalancing and transportation planning considering traffic congestion and uncertainties in disaster response. Comput. Ind. Eng. 149 (2020) 106782. [Google Scholar]
  • X. Gao, X. Jin, P. Zheng and C. Cui, Multi-modal transportation planning for multi-commodity rebalancing under uncertainty in humanitarian logistics. Adv. Eng. Inf. 47 (2021) 101223. [Google Scholar]
  • X. Gao, G. Huang, Q. Zhao, C. Cao and H. Jiang, Robust optimization model for medical staff rebalancing problem with data contamination during COVID-19 pandemic. Int. J. Prod. Res. 60 (2022) 1737–1766. [CrossRef] [Google Scholar]
  • A. Goli and K. Kianfar, Mathematical modeling and fuzzy ε-constraint method for closed-loop mask supply chain design. Sharif J. Ind. Eng. Manag. (2022). [Google Scholar]
  • A. Goli, A. Ala and S. Mirjalili, A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Ann. Oper. Res. (2022) 1–38. [Google Scholar]
  • H.H. Gossen, The laws of human relations and the rules of human action derived therefrom (Translated title: Die entwicklung der gesetze des menschlichen verkehrs und der daraus fließenden regeln für menschliches handeln, przet lumaczony na angielski jako), in Cambridge Handbook on constructing composite indicators. MIT Press (1983). [Google Scholar]
  • A. Haeri, S.-M. Hosseini-Motlagh, M.R.G. Samani and M. Rezaei, A bi-level programming approach for improving relief logistics operations: a real case in Kermanshah earthquake. Comput. Ind. Eng. 145 (2020) 106532. [CrossRef] [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: OR 57 (2023) 59–85. [CrossRef] [EDP Sciences] [Google Scholar]
  • A. Jamali, A. Ranjbar, J. Heydari and S. Nayeri, A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity. Ann. Oper. Res. 319 (2022) 1265–1300. [CrossRef] [MathSciNet] [Google Scholar]
  • Y.-H. Lin, R. Batta, P.A. Rogerson, A. Blatt and M. Flanigan, A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio-Econ. Plan. Sci. 45 (2011) 132–145. [CrossRef] [Google Scholar]
  • Y. Liu, H. Lei, Z. Wu and D. Zhang, A robust model predictive control approach for post-disaster relief distribution’. Comput. Ind. Eng. 135 (2019) 1253–1270. [CrossRef] [Google Scholar]
  • N. Loree and F. Aros-Vera, Points of distribution location and inventory management model for post-disaster humanitarian logistics. Transp. Res. Part E Logist. Transp. Rev. 116 (2018) 1–24. [CrossRef] [Google Scholar]
  • R. Lotfi, K. Kheiri, A. Sadeghi and E.B. Tirkolaee, An extended robust mathematical model to project the course of COVID-19 epidemic in Iran. Ann. Oper. Res. (2022). [Google Scholar]
  • H.O. Mete and Z.B. Zabinsky, Stochastic optimization of medical supply location and distribution in disaster management. Int. J. Prod. Econ. 126 (2010) 76–84. [Google Scholar]
  • S. Mohammadi, S.A. Darestani, B. Vahdani and A. Alinezhad, A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Comput. Ind. Eng. 148 (2020) 106734. [CrossRef] [Google Scholar]
  • A. Nagurney, E.A. Flores and C. Soylu, A generalized nash equilibrium network model for post-disaster humanitarian relief. Transp. Res. Part E Logist. Transp. Rev. 95 (2016) 1–18. [CrossRef] [Google Scholar]
  • W. Ni, J. Shu and M. Song, Location and emergency inventory pre-positioning for disaster response operations: min-max robust model and a case study of Yushu earthquake. Prod. Oper. Manag. 27 (2018) 160–183. [CrossRef] [Google Scholar]
  • S.J. Rennemo, K.F. Ro, L.M. Hvattum and G. Tirado, A three-stage stochastic facility routing model for disaster response planning. Transp. Res. Part E Logist. Transp. Rev. 62 (2014) 116–135. [CrossRef] [Google Scholar]
  • M. Rezaei-Malek and R. Tavakkoli-Moghaddam, Robust humanitarian relief logistics network planning. Uncertain Supply Chain Manag. 2 (2014) 73–96. [CrossRef] [Google Scholar]
  • D. Rivera-Royero, G. Galindo and R. Yie-Pinedo, A dynamic model for disaster response considering prioritized demand points. Socio-Econ. Plan. Sci. 55 (2016) 59–75. [CrossRef] [Google Scholar]
  • L. Rubinson, F. Vaughn, S. Nelson, S. Giordano, T. Kallstrom, T. Buckley, T. Burney, N. Hupert, R. Mutter and M. Handrigan, Mechanical ventilators in US acute care hospitals. Disaster Med. Public Health Prep. 4 (2010) 199–206. [CrossRef] [PubMed] [Google Scholar]
  • A.S. Safaei, S. Farsad and M.M. Paydar, Robust bi-level optimization of relief logistics operations. Appl. Math. Model. 56 (2018) 359–380. [CrossRef] [MathSciNet] [Google Scholar]
  • J.M. Stauffer, A.J. Pedraza-Martinez, L.L. Yan and L.N. Van Wassenhove, Asset supply networks in humanitarian operations: a combined empirical-simulation approach. J. Oper. Manag. 63 (2018) 44–58. [CrossRef] [Google Scholar]
  • H. Sun, Y. Wang and Y. Xue, A bi-objective robust optimization model for disaster response planning under uncertainties. Comput. Ind. Eng. 155 (2021) 107213. [CrossRef] [Google Scholar]
  • E.B. Tirkolaee, P. Abbasian and G.-W. Weber, Sustainable fuzzy multi-trip location-routing problem for medical waste management during the COVID-19 outbreak. Sci. Total Environ. 756 (2021) 143607. [CrossRef] [Google Scholar]
  • E.B. Tirkolaee, A. Goli, P. Ghasemi and F. Goodarzian, Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: pareto-based algorithms. J. Clean. Prod. 333 (2022) 130056. [CrossRef] [Google Scholar]
  • E.B. Tirkolaee, H. Golpîra, 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. Socio-Econ. Plan. Sci. 85 (2023) 101439. [CrossRef] [Google Scholar]
  • Ventilator Stockpiling and Availability in the US. Johns Hopkins Center for Health Security. Johns Hopkins (2020). [Google Scholar]
  • Y. Wang, Y. Wang, Y. Chen and Q. Qin, Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. J. Med. Virol. 92 (2020) 568–576. [CrossRef] [PubMed] [Google Scholar]
  • X. Wei, H. Qiu, D. Wang, J. Duan, Y. Wang and T.C.E. Cheng, An integrated location-routing problem with post-disaster relief distribution. Comput. Ind. Eng. 147 (2020) 106632. [CrossRef] [Google Scholar]
  • World Health Organization (2020). [Google Scholar]

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