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. [CrossRef] [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. [CrossRef] [MathSciNet] [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. [CrossRef] [MathSciNet] [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. [CrossRef] [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]

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.