109
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Globalised robust bilevel model for multi-commodity distribution and vehicle assignment in post-disaster rescue

, & ORCID Icon

References

  • Akbari, F., Valizadeh, J., & Hafezalkotob, A. (2021). Robust cooperative planning of relief logistics operations under demand uncertainty: A case study on a possible earthquake in Tehran. International Journal of Systems Science: Operations & Logistics, 2(4), 31–35. https://doi.org/10.1080/23302674.2021.1914767.
  • Al Theeb, N., & Murray, C. (2017). Vehicle routing and resource distribution in postdisaster humanitarian relief operations. International Transactions in Operational Research, 24(6), 1253–1284. https://doi.org/10.1111/itor.12308
  • Apivatanagul, P., Davidson, R. A., & Nozick, L. K. (2012). Bi-level optimization for risk-based regional hurricane evacuation planning. Natural Hazards, 60(2), 567–588. https://doi.org/10.1007/s11069-011-0029-9
  • Bai, X. J., Li, X., Jia, R. R., & Liu, Y. K. (2019). A distributionally robust credibilistic optimization method for the economic-environmental-energy-social sustainability problem. Information Sciences, 501, 1–18. https://doi.org/10.1016/j.ins.2019.05.031
  • Balcik, B., Silvestri, S., Rancourt, M., & Laporte, G. (2019). Collaborative prepositioning network design for regional disaster response. Production and Operations Management, 28(10), 2431–2455. https://doi.org/10.1111/poms.13053
  • Ben-Tal, A., Boyd, S., & Nemirovski, A. (2006). Extending scope of robust optimization: Comprehensive robust counterparts of uncertain problems. Mathematical Programming, 107(1), 63–89. https://doi.org/10.1007/s10107-005-0679-z
  • Byeon, G., & Hentenryck, P. V. (2022). Benders subproblem decomposition for bilevel problems with convex follower. INFORMS Journal on Computing, 34(3), 1749–1767. https://doi.org/10.1287/ijoc.2021.1128
  • Cao, C., Liu, Y., Tang, O., & Gao, X. (2021). A fuzzy bi-level optimization model for multiperiod post-disaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics, 235, 1–14. https://doi.org/10.1016/j.ijpe.2021.108081.
  • Cheng, J. J., Feng, X. Q., & Bai, X. J. (2021). Modeling equitable and effective distribution problem in humanitarian relief logistics by robust goal programming. Computers & Industrial Engineering, 155, Article 107183. https://doi.org/10.1016/j.cie.2021.107183
  • Doodman, M., Shokr, I., Bozorgi-Amiri, A., & Jolai, F. (2019). Pre-positioning and dynamic operations planning in pre- and post-disaster phases with lateral transshipment under uncertainty and disruption. Journal of Industrial Engineering International, 15(S1), 53–68. https://doi.org/10.1007/s40092-019-0317-7
  • Gao, X. (2019). A bi-level stochastic optimization model for multi-commodity rebalancing under uncertainty in disaster response. Annals of Operations Research, 283(1-2), 1–34. https://doi.org/10.1007/s10479-019-03440-7
  • Goldschmidt, K. H., & Kumar, S. (2017). Reducing the cost of humanitarian operations through disaster preparation and preparedness. Annals of Operations Research, 283(1), 1139–1152. https://doi.org/10.1007/s10479-017-2587-z.
  • Gutjahr, W. J., & Dzubur, N. (2016). Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 85, 1–22. https://doi.org/10.1016/j.tre.2015.11.001
  • Haeri, A., Hosseini-Motlagh, S. M., Samani, M. R. G., & Rezaei, M. (2020). A bi-level programming approach for improving relief logistics operations: A real case in Kermanshah earthquake. Computers & Industrial Engineering, 145, Article 106532. https://doi.org/10.1016/j.cie.2020.106532
  • Jia, R. R., Liu, Y. K., & Bai, X. J. (2020). Sustainable supplier selection and order allocation: Distributionally robust goal programming model and tractable approximation. Computers & Industrial Engineering, 140, Article 106267. https://doi.org/10.1016/j.cie.2020.106267
  • Kamyabniya, A., Lotfi, M. M., Cai, H., Hosseininasab, H., Yaghoubi, S., & Yih, Y. (2019). A two-phase coordinated logistics planning approach to platelets provision in humanitarian relief operations. IISE Transactions, 51(1), 1–21. https://doi.org/10.1080/24725854.2018.1479901
  • Khalili-Damghani, K., Tavana, M., & Ghasemi, P. (2022). A stochastic bi-objective simulationoptimization model for cascade disaster location-allocation-distribution problems. Annals of Operations Research, 309(1), 103–141. https://doi.org/10.1007/s10479-021-04191-0
  • Mahootchi, M., & Golmohammadi, S. (2018). Developing a new stochastic model considering bi-directional relations in a natural disaster: A possible earthquake in Tehran (the capital of Islamic Republic of Iran). Annals of Operations Research, 269(1), 439–473. https://doi.org/10.1007/s10479-017-2596-y
  • Mahtab, Z., Azeem, A., Ali, S. M., Paul, S. K., & Fathollahi-Fard, A. M. (2022). Multi-objective robust-stochastic optimisation of relief goods distribution under uncertainty: A real-life case study. International Journal of Systems Science: Operations & Logistics, 9(2), 241–262. https://doi.org/10.1080/23302674.2021.1879305
  • Mamashli, Z., Bozorgi-Amiri, A., Dadashpour, I., Nayeri, S., & Heydari, J. (2021). A heuristicbased multi-choice goal programming for the stochastic sustainable-resilient routingallocation problem in relief logistics. Neural Computing and Applications, 33(21), 14283–14309. https://doi.org/10.1007/s00521-021-06074-8
  • Mohamed, I. B., Klibi, W., & Vanderbeck, F. (2020). Designing a two-echelon distribution network under demand uncertainty. European Journal of Operational Research, 280(1), 102–123. https://doi.org/10.1016/j.ejor.2019.06.047
  • Mu, G. H., Li, X., Hou, S. S., Lu, Z. Q., & Deng, Y. J. (2020). Injury patterns and outcomes of victims after the 2016 Jiangsu tornado in China: A retrospective analysis of injuries treated at a teaching hospital. Disaster Medicine and Public Health Preparedness, 14(2), 208–213. https://doi.org/10.1017/dmp.2019.43
  • Nezhadroshan, A. M., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2021). A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities. International Journal of Systems Science: Operations & Logistics, 8(4), 321–347. https://doi.org/10.1080/23302674.2020.1769766
  • Ni, W., Shu, J., & Song, M. (2018). Location and emergency inventory pre-positioning for disaster response operations: Min-max robust model and a case study of Yushu earthquake. Production and Operations Management, 27(1), 160–183. https://doi.org/10.1111/poms.12789
  • Perboli, G., Tadei, R., & Vigo, D. (2011). The two-echelon capacitated vehicle routing problem: Models and math-based heuristics. Transportation Science, 45(3), 364–380. https://doi.org/10.1287/trsc.1110.0368
  • Saghehei, E., Memariani, A., & Bozorgi, A. (2021). A bi-level programming approach for prepositioning emergency warehouses. International Journal of Engineering, 34(1), 128–139. https://doi.org/10.5829/IJE.2021.34.01A.15.
  • Sakawa, M., Nishizaki, I., & Uemura, Y. (2002). A decentralized two-level transportation problem in a housing material manufacturer: Interactive fuzzy programming approach. European Journal of Operational Research, 141(1), 167–185. https://doi.org/10.1016/S0377-2217(01)00273-9
  • Souza, J. S., Lim-Apo, F. A., Varella, L., Coelho, A. S., & Souza, J. C. (2022). Multi-period optimization model for planning people allocation in shelters and distributing aid with special constraints. Socio-Economic Planning Sciences, 79, Article 101087. https://doi.org/10.1016/j.seps.2021.101087
  • Sun, H. L., Wang, X. Q., & Xue, Y. F. (2012). A bi-level programming model for a multi-facility location-routing problem in urban emergency system. Engineering Education and Management, 111, 75–80. https://doi.org/10.1007/978-3-642-24823-8_12
  • Vallejo, J., Rodrguez, E. G., Almaguer, F. J., & Ramrez, R. G. (2015). A bi-level optimization model for aid distribution after the occurrence of a disaster. Journal of Cleaner Production, 105, 134–145. https://doi.org/10.1016/j.jclepro.2014.09.069
  • Vieira, Y. E. M., de Mello Bandeira, R. A., & da Silva Jnior, O. S. (2021). Multi-depot vehicle routing problem for large scale disaster relief in drought scenarios: The case of the Brazilian northeast region. International Journal of Disaster Risk Reduction, 58, Article 102193. https://doi.org/10.1016/j.ijdrr.2021.102193
  • Xu, X., Qi, Y., & Hua, Z. (2010). Forecasting demand of commodities after natural disasters. Expert Systems with Applications, 37(6), 4313–4317. https://doi.org/10.1016/j.eswa.2009.11.069
  • Yang, M., Liu, Y., & Yang, G. (2021). Multi-period dynamic distributionally robust prepositioning of emergency supplies under demand uncertainty. Applied Mathematical Modelling, 89, 1433–1458. https://doi.org/10.1016/j.apm.2020.08.035
  • Yuan, Y., Song, Q., & Zhou, B. (2022). Emergency medical service location problem based on physical bounds using chance-constrained programming approach. International Journal of Systems Science, 54(3), 1–13. https://doi.org/10.1080/00207721.2022.2141593.
  • Zheng, Y. J., Chen, S. Y., & Ling, H. F. (2015). Evolutionary optimization for disaster relief operations: A survey. Applied Soft Computing, 27, 553–566. https://doi.org/10.1016/j.asoc.2014.09.041
  • Zheng, Y. J., & Ling, H. F. (2013). Emergency transportation planning in disaster relief supply chain management: A cooperative fuzzy optimization approach. Soft Computing, 17(7), 1301–1314. https://doi.org/10.1007/s00500-012-0968-4
  • Zheng, Y. J., Ling, H. F., & Xue, J. Y. (2018). Disaster rescue task scheduling: An evolutionary multiobjective optimization approach. IEEE Transactions on Emerging Topics in Computing, 6(2), 288–300. https://doi.org/10.1109/TETC.2014.2369957
  • Zheng, Y. J., Yu, S. L., Song, Q., Huang, Y. J., Sheng, W. G., & Chen, S. Y. (2022). Coevolutionary fuzzy deep transfer learning for disaster relief demand forecasting. IEEE Transactions on Emerging Topics in Computing, 10(3), 1361–1373. https://doi.org/10.1109/TETC.2021.3085337
  • Zhong, S., Cheng, R., Jiang, Y., Wang, Z., Larsen, A., & Nielsen, O. A. (2020). Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand. Transportation Research Part E: Logistics and Transportation Review, 141, Article 102015. https://doi.org/10.1016/j.tre.2020.102015
  • Zhou, Y., Liu, J., Zhang, Y., & Gan, X. (2017). A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 99, 77–95. https://doi.org/10.1016/j.tre.2016.12.011

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.