EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 59 / No 3 (May-June 2025) RAIRO-Oper. Res., 59 3 (2025) 1501-1525 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 3, May-June 2025
Page(s) 1501 - 1525
DOI https://doi.org/10.1051/ro/2025052
Published online 04 June 2025
  • D. Alem, A. Clark and A. Moreno, Stochastic network models for logistics planning in disaster relief. Eur. J. Oper. Res. 255 (2016) 187–206. [CrossRef] [Google Scholar]
  • M. Alizadeh, M. Amiri-Aref, N. Mustafee and S. Matilal, A robust stochastic Casualty Collection Points location problem. Eur. J. Oper. Res. 279 (2019) 965–983. [CrossRef] [Google Scholar]
  • N. Aydin and Z. Cetinkale, Simultaneous response to multiple disasters: integrated planning for pandemics and large-scale earthquakes. Int. J. Disaster Risk Reduct. 86 (2023) 103538. [Google Scholar]
  • S¸.Y. Balaman, Investment planning and strategic management of sustainable systems for clean power generation: an ε-constraint based multi objective modelling approach. J. Clean. Prod. 137 (2016) 1179–1190. [Google Scholar]
  • A. Ben-Tal, L. EI Ghaoui and A. Nemirovski, Robust Optimization. Princeton University Press, Princeton (2009). [Google Scholar]
  • D. Bertsimas and M. Sim, The price of robustness. Oper. Res. 52 (2004) 35–53. [Google Scholar]
  • A.M. Caunhye and X. Nie, A stochastic programming model for casualty response planning during catastrophic health events. Transp. Sci. 52 (2018) 437–453. [Google Scholar]
  • N.D.R.C.O.T.P. China, Integrated Analysis and Assessment on Wenchuan Earthquake Disaster. Science Press, Beijing (2018). [Google Scholar]
  • A. Diabat, A. Jabbarzadeh and A. Khosrojerdi, A perishable product supply chain network design problem with reliability and disruption considerations. Int. J. Prod. Econ. 212 (2019) 125–138. [Google Scholar]
  • R. Dubey, A. Gunasekaran and T. Papadopoulos, Disaster relief operations: past, present and future. Ann. Oper. Res. 283 (2019) 1–8. [CrossRef] [MathSciNet] [Google Scholar]
  • O. Ergun, G. Karakus, P. Keskinocak, J. Swann and M. Villarreal, Operations research to improve disaster supply chain management. Oper. Res. Manag. Sci. (2010). DOI: 10.1002/9780470400531.eorms0604. [Google Scholar]
  • R.Z. Farahani, M.M. Lotfi, A. Baghaian, R. Ruiz and S. Rezapour, Mass casualty management in disaster scene: a systematic review of OR&MS research in humanitarian operations. Eur. J. Oper. Res. 287 (2020) 787–819. [CrossRef] [Google Scholar]
  • L. Fazli, A novel two-stage stochastic programming model to design an integrated disaster relief supply chain network-a case study. Oper. Manag. Res. 17 (2024) 1295–1327. [Google Scholar]
  • P. Ghasemi, F. Goodarzian, J. Mu˜nuzuri and A. Abraham, A cooperative game theory approach for location–routing–inventory decisions in humanitarian relief chain incorporating stochastic planning. Appl. Math. Mod. 104 (2022) 750–781. [CrossRef] [Google Scholar]
  • S.M. Hatefi and F. Jolai, Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Appl. Math. Mod. 38 (2014) 2630–2647. [CrossRef] [Google Scholar]
  • J. Holguín-Veras, N. Pérez, M. Jaller, L.N. Van Wassenhove and F. Aros-Vera, On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Manag. 31 (2013) 262–280. [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: Logist. Transp. Rev. 70 (2014) 225–244. [Google Scholar]
  • A. Jabbarzadeh, B. Fahimnia, J.B. Sheu and H.S. Moghadam, Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp. Res. Part B: Methodol. 94 (2016) 121–149. [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) 1–36. [Google Scholar]
  • A. Kaveh and M. Ghobadi, A multistage algorithm for blood banking supply chain allocation problem. Int. J. Civ. Eng. 15 (2017) 103–112. [Google Scholar]
  • S.C.H. Leung, S.O.S. Tsang, W.L. Ng and Y. Wu, A robust optimization model for multi-site production planning problem in an uncertain environment. Eur. J. Oper. Res. 181 (2007) 224–238. [CrossRef] [Google Scholar]
  • H.L. Li, An efficient method for solving linear goal programming problems. J. Optim. Theory Appl. 90 (1996) 465–469. [Google Scholar]
  • Y. Li, J. Zhang and G. Yu, A scenario-based hybrid robust and stochastic approach for joint planning of relief logistics and casualty distribution considering secondary disasters. Transp. Res. Part E: Logist. Transp. Rev. 141 (2020) 102029. [Google Scholar]
  • K. Liu, L. Yang, Y. Zhao and Z.H. Zhang, Multi-period stochastic programming for relief delivery considering evolving transportation network and temporary facility relocation/closure. Transp. Res. Part E: Logist. Transp. Rev. 180 (2023) 103357. [Google Scholar]
  • S. Long, D. Zhang, Y. Liang, S. Li and W. Chen, Robust optimization of the multi-objective multi-period location-routing problem for epidemic logistics system with uncertain demand. IEEE Access 9 (2021) 151912–151930. [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. [Google Scholar]
  • G. Mavrotas, Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213 (2009) 455–465. [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]
  • J.M. Mulvey, R.J. Vanderbei and S.A. Zenios, Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. [Google Scholar]
  • S. Negi and G. Negi, Framework to manage humanitarian logistics in disaster relief supply chain management in India. Int. J. Emerg. Serv. 10 (2021) 40–76. [Google Scholar]
  • M.R. Norouzi, A. Ahmadi, A.E. Nezhad and A. Ghaedi, Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment. Renew. Sustain. Energy Rev. 29 (2014) 911–923. [Google Scholar]
  • P. Peng, L.V. Snyder, A. Lim and Z. Liu, Reliable logistics networks design with facility disruptions. Transp. Res. Part B: Methodol. 45 (2011) 1190–1211. [CrossRef] [Google Scholar]
  • S.K. Rahimi and D. Rahmani, An improved ALNS for hybrid pickup and drones delivery system in disaster by penalizing deprivation time. Comput. Oper. Res. 170 (2024) 106722. [CrossRef] [Google Scholar]
  • D. Rahmani, Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Ann. Oper. Res. 283 (2019) 613–641. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Rezaei-Malek, R. Tavakkoli-Moghaddam, B. Zahiri and A. Bozorgi-Amiri, An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Comput. Ind. Eng. 94 (2016) 201–215. [CrossRef] [Google Scholar]
  • A. Rezvani, M. Gandomkar, M. Izadbakhsh and A. Ahmadi, Environmental/economic scheduling of a micro-grid with renewable energy resources. J. Clean. Prod. 87 (2015) 216–226. [Google Scholar]
  • A.S. Safaei, S. Farsad and M.M. Paydar, Robust bi-level optimization of relief logistics operations. Appl. Math. Mod. 56 (2018) 359–380. [CrossRef] [Google Scholar]
  • S.M. Shavarani, Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution: a case study. J. Humanit. Logist. 9 (2019) 70–81. [Google Scholar]
  • Z.J.M. Shen, R.L. Zhan and J. Zhang, The reliable facility location problem: formulations, heuristics, and approximation algorithms. INFORMS J. Comput. 23 (2011) 470–482. [CrossRef] [MathSciNet] [Google Scholar]
  • I. Shokr, F. Jolai and A. Bozorgi-Amiri, A novel humanitarian and private sector relief chain network design model for disaster response. Int. J. Disaster Risk Reduct. 65 (2021) 102522. [Google Scholar]
  • L.V. Snyder, Facility location under uncertainty: a review. IIE Trans. 38 (2006) 547–564. [CrossRef] [Google Scholar]
  • A.L. Soyster, Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper. Res. 21 (1973) 1154–1157. [CrossRef] [Google Scholar]
  • H. Sun, J. Li, T. Wang and Y. Xue, A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions. Transp. Res. Part E: Logist. Transp. Rev. 157 (2022) 102578. [Google Scholar]
  • C. Wang and L.M.T. Zhong, Reliable design of humanitarian supply chain under correlated disruptions: a two-stage distributionally robust approach. Ann. Oper. Res. (2024) 1–49. [Google Scholar]
  • B.C. Wang, Q.Y. Qian, J.J. Gao, Z.Y. Tan and Y. Zhou, The optimization of warehouse location and resources distribution for emergency rescue under uncertainty. Adv. Eng. Inf. 48 (2021) 101278. [CrossRef] [Google Scholar]
  • Q. Wang, Z. Liu, P. Jiang and L. Luo, A stochastic programming model for emergency supplies pre-positioning, transshipment and procurement in a regional healthcare coalition. Socio-Econ. Plann. Sci. 82 (2022) 101279. [Google Scholar]
  • M. Yahyaei and A. Bozorgi-Amiri, Robust reliable humanitarian relief network design: an integration of shelter and supply facility location. Ann. Oper. Res. 283 (2019) 897–916. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Yang, Y. Liu and G. Yang, Multi-period dynamic distributionally robust pre-positioning of emergency supplies under demand uncertainty. Appl. Math. Mod. 89 (2021) 1433–1458. [CrossRef] [Google Scholar]
  • Y. Yang, Y. Yin, D. Wang, J. Ignatius, T.C.E. Cheng and L. Dhamotharan, Distributionally robust multi-period location–allocation with multiple resources and capacity levels in humanitarian logistics. Eur. J. Oper. Res. 305 (2023) 1042–1062. [CrossRef] [Google Scholar]
  • M. Yang, S. Kumar, X. Wang and M.J. Fry, Scenario-robust pre-disaster planning for multiple relief items. Ann. Oper. Res. 335 (2024) 1–26. [Google Scholar]
  • R. Yang, Y. Li, B. Zhang and R. Yang, Location–allocation problem in the emergency logistics system considering lateral transshipment strategy. Comput. Ind. Eng. 187 (2024) 109771. [CrossRef] [Google Scholar]
  • H. Yang, Y. Yang, D. Wang, T.C.E. Cheng, Y. Yin and H. Hu, A scenario-based robust approach for joint planning of multi-blood product logistics and multi-casualty type evacuation. Transp. Res. Part E: Logist. Transp. Rev. 184 (2024) 103493. [Google Scholar]
  • Y. Yin, X. Xu, D. Wang, Y. Yu and T.C.E. Cheng, Two-stage recoverable robust optimization for an integrated location–allocation and evacuation planning problem. Transp. Res. Part B: Methodol. 182 (2024) 102906. [Google Scholar]
  • C.S. Yu and H.L. Li, A robust optimization model for stochastic logistic problems. Int. J. Prod. Econ. 64 (2000) 385–397. [Google Scholar]
  • Y. Yuan, Impact of intensity and loss assessment following the great Wenchuan Earthquake. Earthq. Eng. Eng. Vib. 7 (2008) 247–254. [CrossRef] [Google Scholar]
  • K. Zaman, M. McDonald, S. Mahadevan and L. Green, Robustness-based design optimization under data uncertainty. Struct. Multidiscip. Optim. 44 (2011) 183–197. [Google Scholar]
  • L. Zhang and N. Cui, Pre-positioning facility location and resource allocation in humanitarian relief operations considering deprivation costs. Sustainability 13 (2021) 4141. [Google Scholar]
  • D. Zhang, Y. Zhang, S. Li and S. Li, A novel min–max robust model for post-disaster relief kit assembly and distribution. Expert Syst. App. 214 (2023) 119198. [CrossRef] [Google Scholar]
  • Y. Zhou, Y. Gong, X. Hu and C. Liu, Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief. Grey Syst.: Theory Appl. 13 (2022) 322–339. [Google Scholar]
  • L. Zhu, Y. Gong, Y. Xu and J. Gu, Emergency relief routing models for injured victims considering equity and priority. Ann. Oper. Res. 283 (2019) 1573–1606. [CrossRef] [MathSciNet] [Google Scholar]
  • T. Zhu, S. D. Boyle and A. Unnikrishnan, Two-stage robust facility location problem with drones. Transp. Res. Part C: Emerg. Technol. 137 (2022) 103563. [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.