Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 3, May-June 2021
Page(s) 1865 - 1883
DOI https://doi.org/10.1051/ro/2021086
Published online 22 June 2021
  • H. Abidi, S. de Leeuw and M. Klumpp, Humanitarian supply chain performance management: a systematic literature review. Supply Chain Manage. Int. J. 19 (2014) 592–608. [Google Scholar]
  • M. Ahmadi, A. Seifi and B. Tootooni, A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: a case study on San Francisco district. Transp. Res. Part E: Logist. Transp. Rev. 75 (2015) 145–163. [Google Scholar]
  • A. Ahmadi-Javid, P. Seyedi and S. Syam, A survey of healthcare facility location. Comput. Oper. Res. 79 (2017) 223–263. [Google Scholar]
  • H. Aissi, C. Bazgan and D. Vanderpooten, Min-max and min-max regret versions of combinatorial optimization problems: a survey. Eur. J. Oper. Res. 197 (2009) 427–438. [Google Scholar]
  • R. Banomyong, P. Varadejsatitwong and R. Oloruntoba, A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016. Ann. Oper. Res. 2832019 (2005) 71–86. [Google Scholar]
  • R. Benkoczi, B. Bhattacharya, Y. Higashikawa, T. Kameda and N. Katoh, Minmax-regret evacuation planning for cycle networks. In: International Conference on Theory and Applications of Models of Computation. Springer (2019) 42–58. [Google Scholar]
  • A. Ben-Tal, B. Do Chung, S. Mandala and T. Yao, Robust optimization for emergency logistics planning: risk mitigation in humanitarian relief supply chains. Transp. Res. Part B Methodol. 45 (2011) 1177–1189. [Google Scholar]
  • B. Bhattacharya and T. Kameda, Improved algorithms for computing minmax regret sinks on dynamic path and tree networks. Theor. Comput. Sci. 607 (2015) 411–425. [Google Scholar]
  • B. Bhattacharya, Y. Higashikawa, T. Kameda and N. Katoh, Minmax regret 1-sink for aggregate evacuation time on path networks. Preprint arXiv:1806.00814 (2018). [Google Scholar]
  • C. Boonmee, M. Arimura and T. Asada, Facility location optimization model for emergency humanitarian logistics. Int. J. Disaster Risk Reduct. 24 (2017) 485–498. [Google Scholar]
  • A. Bozorgi-Amiri, M. Jabalameli and S. Al-e Hashem, A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectr. 35 (2013) 905–933. [Google Scholar]
  • A. Candia-Vejar, E. Alvarez-Miranda and N. Maculan, Minmax regret combinatorial optimization problems: an algorithmic perspective. RAIRO: OR 45 (2011) 101–129. [Google Scholar]
  • A. Caunhye, X. Nie and S. Pokharel, Optimization models in emergency logistics: a literature review. Socio-Econ. Plan. Sci. 46 (2012) 4–13. [Google Scholar]
  • A. Chen, Z. Zhou, P. Chootinan, S. Ryu, C. Yang and S. Wong, Transport network design problem under uncertainty: a review and new developments. Transp. Rev. 31 (2011) 743–768. [Google Scholar]
  • J. Chu and S. Chen, Optimization of transportation-infrastructure-system protection considering weighted connectivity reliability. J. Infrastruct. Syst. 22 (2015) 04015008. [Google Scholar]
  • E. Conde and M. Leal, Minmax regret combinatorial optimization problems with investments. Comput. Oper. Res. 85 (2017) 1–11. [Google Scholar]
  • E. Dijkstra, A note on two problems in connexion with graphs. Numer. Math. 1 (1959) 269–271. [Google Scholar]
  • L. Du and S. Peeta, A stochastic optimization model to reduce expected post-disaster response time through pre-disaster investment decisions. Networks Spatial Econ. 14 (2014) 271–295. [Google Scholar]
  • M. Fereiduni and K. Shahanaghi, A robust optimization model for distribution and evacuation in the disaster response phase. J. Ind. Eng. Int. 13 (2017) 117–141. [Google Scholar]
  • V. Gabrel, C. Murat and A. Thiele, Recent advances in robust optimization: an overview. Eur. J. Oper. Res. 235 (2014) 471–483. [Google Scholar]
  • S. Ghavami, Multi-criteria spatial decision support system for identifying strategic roads in disaster situations. Int. J. Crit. Infrastruct. Prot. 24 (2019) 23–36. [Google Scholar]
  • W. Gutjahr and P. Nolz, Multicriteria optimization in humanitarian aid. Eur. J. Oper. Res. 252 (2016) 351–366. [Google Scholar]
  • P. Hart, N.J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4 (1968) 100–107. [Google Scholar]
  • C. Higgins, M.N. Sweet and P. Kanaroglou, All minutes are not equal: travel time and the effects of congestion on commute satisfaction in Canadian cities. Transportation 45 (2018) 1249–1268. [Google Scholar]
  • J. Holguίn-Veras, N. Pérez, M. Jaller, L. Van Wassenhove and F. Aros-Vera, On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Manage. 31 (2013) 262–280. [Google Scholar]
  • M. Hoyos, R. Morales and R. Akhavan-Tabatabaei, Or models with stochastic components in disaster operations management: a literature survey. Comput. Ind. Eng. 82 (2015) 183–197. [Google Scholar]
  • C.J.C. Jabbour, V.A. Sobreiro, A.B.L. de Sousa Jabbour, L.M. de Souza Campos, E.B. Mariano and D.W.S. Renwick, An analysis of the literature on humanitarian logistics and supply chain management: paving the way for future studies. Ann. Oper. Res. 283 (2019) 289–307. [Google Scholar]
  • N. Javadian, S. Modarres and A. Bozorgi, A bi-objective stochastic optimization model for humanitarian relief chain by using evolutionary algorithms. Int. J. Eng. Trans. A: Basics 30 (2017) 1526–1537. [Google Scholar]
  • A. Kasperski, Discrete Optimization with Interval Data. In: Vol. 228 of Studies in Fuzziness and Soft Computing. Springer Berlin Heidelberg, Berlin, Heidelberg (2008). [Google Scholar]
  • P. Kouvelis and G. Yu, Robust Discrete Optimization and its Applications. Kluwer Academic Publishers (1997). [Google Scholar]
  • G. Kovacs and M. Moshtari, A roadmap for higher research quality in humanitarian operations: a methodological perspective. Eur. J. Oper. Res. 276 (2019) 395–408. [Google Scholar]
  • Y. Li and S. Chung, Disaster relief routing under uncertainty: a robust optimization approach. IISE Trans. 51 (2019) 869–886. [Google Scholar]
  • S. Liu, Y. Peng, Q. Song and Y. Zhong, The robust shortest path problem for multimodal transportation considering timetable with interval data. Syst. Sci. Control Eng. 6 (2018) 68–78. [Google Scholar]
  • R. Montemanni and L. Gambardella, An exact algorithm for the robust shortest path problem with interval data. Comput. Oper. Res. 31 (2004) 1667–1680. [Google Scholar]
  • R. Montemanni, L. Gambardella and A. Donati, A branch and bound algorithm for the robust shortest path problem with interval data. Oper. Res. Lett. 32 (2004) 225–232. [Google Scholar]
  • N. Nikoo, M. Babaei and A. Mohaymany, Emergency transportation network design problem: identification and evaluation of disaster response routes. Int. J. Disaster Risk Reduct. 27 (2018) 7–20. [Google Scholar]
  • M. Ortuňo, P. Cristóbal, J. Ferrer, F. Martίn-Campo, S. Muňoz, G. Tirado and B. Vitoriano, Decision aid models and systems for humanitarian logistics. A survey. In: Decision Aid Models for Disaster Management and Emergencies. Springer (2013) 17–44. [Google Scholar]
  • L. Özdamar and M. Ertem, Models, solutions and enabling technologies in humanitarian logistics. Eur. J. Oper. Res. 244 (2015) 55–65. [Google Scholar]
  • S. Peeta, F. Salman, D. Gunnec and K. Viswanath, Pre-disaster investment decisions for strengthening a highway network. Comput. Oper. Res. 37 (2010) 1708–1719. [Google Scholar]
  • E. Peres, I. Brito, A. Leiras and H. Yoshizaki, Humanitarian logistics and disaster relief research: trends, applications, and future research directions. In: Proceedings of the 4th International Conference on Information Systems, Logistics and Supply Chain (2012) 26–29. [Google Scholar]
  • F. Pérez-Galarce, L. Canales, C. Vergara and A. Candia-Véjar, An optimization model for the location of disaster refuges. Socio-Econ. Plan. Sci. 59 (2017) 56–66. [Google Scholar]
  • F. Pérez-Galarce, A. Candia-Véjar, C. Astudillo and M. Bardeen, Algorithms for the minmax regret path problem with interval data. Inf. Sci. 462 (2018) 218–241. [Google Scholar]
  • A. Ruszczyński and A. Shapiro, Stochastic programming models. In: Vol. 10 of Handbooks in Operations Research and Management Science (2003) 1–64. [Google Scholar]
  • C. Shi, B. Chen and Q. Li, Estimation of travel time distributions in urban road networks using low-frequency floating car data. ISPRS Int. J. Geo-Inf. 6 (2017) 253. [Google Scholar]
  • S. Tofighi, S. Torabi and S. Mansouri, Humanitarian logistics network design under mixed uncertainty. Eur. J. Oper. Res. 250 (2016) 239–250. [Google Scholar]
  • B. Vahdani, D. Veysmoradi, F. Noori and F. Mansour, Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. Int. J. Disaster Risk Reduct. 27 (2018) 290–306. [Google Scholar]
  • H. Wang, Minmax regret 1-facility location on uncertain path networks. Eur. J. Oper. Res. 239 (2014) 636–643. [Google Scholar]
  • Q. Wang and X. Nie, A stochastic programming model for emergency supply planning considering traffic congestion. IISE Trans. 51 (2019) 910–920. [Google Scholar]

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