Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 6, November-December 2021
Page(s) 3399 - 3426
DOI https://doi.org/10.1051/ro/2021144
Published online 15 November 2021
  • S. Ajami and M. Fattahi, The role of earthquake information management systems (EIMSs) in reducing destruction: A comparative study of Japan, Turkey and Iran. Disaster Prev. Manag. An Int. J. 18 (2009) 150–161. [CrossRef] [Google Scholar]
  • N. Altay and W.G. Green, OR/MS research in disaster operations management. Eur. J. Oper. Res. 175 (2006) 475–493. [CrossRef] [Google Scholar]
  • M. Afzalirad and J. Rezaeian, A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches. Appl. Soft Comput. 50 (2017) 109–123. [CrossRef] [Google Scholar]
  • B. Balcik, B.M. Beamon, C.C. Krejci, K.M. Muramatsu and M. Ramirez, Coordination in humanitarian relief chains: Practices, challenges and opportunities. Int. J. Prod. Econ. 126 (2010) 22–34. [CrossRef] [Google Scholar]
  • T. Bektas, The multiple traveling salesman problem: An overview of formulations and solution procedures. Omega 34 (2006) 209–219. [CrossRef] [Google Scholar]
  • H. Billhardt, M. Lujak, V. Sánchez-Brunete, A. Fernández and S. Ossowski, Dynamic coordination of ambulances for emergency medical assistance services. Knowledge-Based Syst. 70 (2014) 268–280. [CrossRef] [Google Scholar]
  • D. Biskup, Single-machine scheduling with learning considerations. Eur. J. Oper. Res. 115 (1999) 173–178. [Google Scholar]
  • D. Biskup, A state-of-the-art review on scheduling with learning effects. Eur. J. Oper. Res. 188 (2008) 315–329. [Google Scholar]
  • B. Bodaghi, E. Palaneeswaran, S. Shahparvari and M. Mohammadi, Probabilistic allocation and scheduling of multiple resources for emergency operations; a Victorian bushfire case study. Comput. Environ. Urban Syst. 81 (2020) 101479. [CrossRef] [Google Scholar]
  • J.-F. Camacho-Vallejo, E. González-Rodrguez, F.-J. Almaguer and R.G. González-Ramrez, A bi-level optimization model for aid distribution after the occurrence of a disaster. J. Clean. Prod. 105 (2015) 134–145. [CrossRef] [Google Scholar]
  • A.M. Campbell and P.C. Jones, Prepositioning supplies in preparation for disasters. Eur. J. Oper. Res. 209 (2011) 156–165. [CrossRef] [Google Scholar]
  • V. Cantillo, I. Serrano, L.F. Macea and J. Holgun-Veras, Discrete choice approach for assessing deprivation cost in humanitarian relief operations. Socioecon. Plann. Sci. 63 (2018) 33–46. [CrossRef] [Google Scholar]
  • Y. Chen, Q. Zhao, L. Wang and M. Dessouky, The regional cooperation-based warehouse location problem for relief supplies. Comput. Ind. Eng. 102 (2016) 259–267. [CrossRef] [Google Scholar]
  • T.C.E. Cheng and G. Wang, Single machine scheduling with learning effect considerations. Ann. Oper. Res. 98 (2000) 273–290. [CrossRef] [MathSciNet] [Google Scholar]
  • T.C.E. Cheng, C.-C. Wu and W.-C. Lee, Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects. Inf. Sci. (Ny) 178 (2008) 2476–2487. [CrossRef] [Google Scholar]
  • C. Chinnarasri and K. Phothiwijit, Appropriate engineering measures with participation of community for flood disaster reduction: Case of the Tha Chin Basin, Thailand. Arab. J. Sci. Eng. 41 (2016) 4879–4892. [CrossRef] [Google Scholar]
  • L.K. Comfort, K. Ko and A. Zagorecki, Coordination in rapidly evolving disaster response systems: The role of information. Am. Behav. Sci. 48 (2004) 295–313. [CrossRef] [Google Scholar]
  • V. Cunha, L. Pessoa, M. Vellasco, R. Tanscheit and M.A. Pacheco, A biased random-key genetic algorithm for the rescue unit allocation and scheduling problem. In: 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE (2018) 1–6. [Google Scholar]
  • K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2002) 182–197. [CrossRef] [Google Scholar]
  • R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE (1995) 39–43. [Google Scholar]
  • M. Falasca, C.W. Zobel and G.M. Fetter, An optimization model for humanitarian relief volunteer management. In: Proceedings of the 6th International ISCRAM Conference (2009). [Google Scholar]
  • F. Fiedrich, F. Gehbauer and U. Rickers, Optimized resource allocation for emergency response after earthquake disasters. Saf. Sci. 35 (2000) 41–57. [CrossRef] [Google Scholar]
  • P. Gasparini, G. Manfredi and J. Zschau, Earthquake early warning systems. Springer (2007). [CrossRef] [Google Scholar]
  • F. Glover and E. Woolsey, Technical note – Converting the 0–1 polynomial programming problem to a 0–1 linear program. Oper. Res. 22 (1974) 180–182. [CrossRef] [Google Scholar]
  • M. Grabowski, C. Rizzo and T. Graig, Data challenges in dynamic, large-scale resource allocation in remote regions. Saf. Sci. 87 (2016) 76–86. [CrossRef] [Google Scholar]
  • J. Gu, Y. Zhou, A. Das, I. Moon and G. Lee, Medical relief shelter location problem with patient severity under a limited relief budget. Comput. Ind. Eng. 125 (2018) 720–728. [CrossRef] [Google Scholar]
  • K. Huang, Y. Jiang, Y. Yuan and L. Zhao, Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transp. Res. Part E Logist. Transp. Rev. 75 (2015) 1–17. [CrossRef] [Google Scholar]
  • M. Huang, K. Smilowitz and B. Balcik, Models for relief routing: Equity, efficiency and efficacy. Transp. Res. Part E Logist. Transp. Rev. 48 (2012) 2–18. [CrossRef] [Google Scholar]
  • P.-J. Lai and W.-C. Lee, Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects. Omega 39 (2011) 467–471. [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]
  • G. Mavrotas, Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213 (2009) 455–465. [Google Scholar]
  • M. Musavi and A. Bozorgi-Amiri, A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Comput. Ind. Eng. 113 (2017) 766–778. [CrossRef] [Google Scholar]
  • M. Najafi, K. Eshghi and W. Dullaert, A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transp. Res. Part E Logist. Transp. Rev. 49 (2013) 217–249. [CrossRef] [Google Scholar]
  • S. Nayeri, E. Asadi-Gangraj and S. Emami, Metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect. Neural Comput. Appl. 31 (2019) 7517–7537. [CrossRef] [Google Scholar]
  • F. Nisha de Silva, Providing spatial decision support for evacuation planning: A challenge in integrating technologies. Disaster Prev. Manag. An Int. J. 10 (2001) 11–20. [CrossRef] [Google Scholar]
  • N. Noyan, Risk-averse two-stage stochastic programming with an application to disaster management. Comput. Oper. Res. 39 (2012) 541–559. [CrossRef] [MathSciNet] [Google Scholar]
  • G.S. Peace, Taguchi methods: A hands-on approach. Addison Wesley Publishing Company (1993). [Google Scholar]
  • E. Pollak, M. Falash, L. Ingraham and V. Gottesman, Operational analysis framework for emergency operations center preparedness training. In: Proceedings 36th Winter Simulation Conference (2004) 839–848. [Google Scholar]
  • R. Pradhananga, F. Mutlu, S. Pokharel, J. Holgun-Veras and D. Seth, An integrated resource allocation and distribution model for pre-disaster planning. Comput. Ind. Eng. 91 (2016) 229–238. [CrossRef] [Google Scholar]
  • S.H.A. Rahmati, V. Hajipour and S.T.A. Niaki, A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl. Soft Comput. 13 (2013) 1728–1740. [Google Scholar]
  • K. Ransikarbum and S.J. Mason, Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. Int. J. Prod. Econ. 182 (2016) 324–341. [CrossRef] [Google Scholar]
  • G. Rauchecker and G. Schryen, An exact branch-and-price algorithm for scheduling rescue units during disaster response. Eur. J. Oper. Res. 272 (2019) 352–363. [CrossRef] [Google Scholar]
  • C.G. Rawls and M.A. Turnquist, Pre-positioning of emergency supplies for disaster response. Transp. Res. Part B Methodol. 44 (2010) 521–534. [CrossRef] [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]
  • E. Rolland, R.A. Patterson, K. Ward and B. Dodin, Decision support for disaster management. Oper. Manag. Res. 3 (2010) 68–79. [CrossRef] [Google Scholar]
  • K. Saleem, S. Luis, Y. Deng, S.-C. Chen, V. Hristidis and T. Li, Towards a business continuity information network for rapid disaster recovery. In: Proceedings of the 2008 International Conference on Digital Government Society of North America (2008) 107–116. [Google Scholar]
  • A. Santoso, R.A.P. Sutanto, D.N. Prayogo and J. Parung, Development of fuzzy RUASP model – Grasp metaheuristics with time window: Case study of Mount Semeru eruption in East Java. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing (2019) 12081. [Google Scholar]
  • J. Sathish Kumar and M.A. Zaveri, Resource scheduling for postdisaster management in IoT environment. Wirel. Commun. Mob. Comput. (2019) 7802843. [Google Scholar]
  • B. Shahidi-Zadeh, R. Tavakkoli-Moghaddam, A. Taheri-Moghadam and I. Rastgar, Solving a bi-objective unrelated parallel batch processing machines scheduling problem: A comparison study. Comput. Oper. Res. 88 (2017) 71–90. [CrossRef] [MathSciNet] [Google Scholar]
  • S.M. Shavarani, M. Golabi, B. Vizvari, Assignment of medical staff to operating rooms in disaster preparedness: A novel stochastic approach. IEEE Trans. Eng. Manag. 67 (2019) 593–602. [Google Scholar]
  • J.-B. Sheu, An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transp. Res. Part E Logist. Transp. Rev. 43 (2007) 687–709. [CrossRef] [Google Scholar]
  • J.-B. Sheu, Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transp. Res. Part E Logist. Transp. Rev. 46 (2010) 1–17. [CrossRef] [Google Scholar]
  • I. Sung and T. Lee, Optimal allocation of emergency medical resources in a mass casualty incident: patient prioritization by column generation. Eur. J. Oper. Res. 252 (2016) 623–634. [CrossRef] [Google Scholar]
  • A. Svensson, J. Holst, R. Lindquist and G. Lindgren, Optimal prediction of catastrophes in autoregressive moving-average processes. J. Time Ser. Anal. 17 (1996) 511–531. [CrossRef] [MathSciNet] [Google Scholar]
  • G. Taguchi, Introduction to quality engineering: Designing quality into products and processes. Unipub/Kraus (1986). [Google Scholar]
  • H. Tamura, K. Yamamoto, S. Tomiyama and I. Hatono, Modeling and analysis of decision making problem for mitigating natural disaster risks. Eur. J. Oper. Res. 122 (2000) 461–468. [CrossRef] [Google Scholar]
  • S.A. Torabi, N. Sahebjamnia, S.A. Mansouri and M.A. Bajestani, A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Appl. Soft Comput. 13 (2013) 4750–4762. [CrossRef] [Google Scholar]
  • UN ISDR, Hyogo framework for action 2005–2015: Building the resilience of nations and communities to disasters. In: Extract from the final report of the World Conference on Disaster Reduction (A/CONF. 206/6) (2005). [Google Scholar]
  • A.A. Visheratin, M. Melnik, D. Nasonov, N. Butakov and A.V. Boukhanovsky, Hybrid scheduling algorithm in early warning systems. Future Gener. Comput. Syst. 79 (2018) 630–642. [CrossRef] [Google Scholar]
  • B. Vitoriano, M.T. Ortuño, G. Tirado and J. Montero, A multi-criteria optimization model for humanitarian aid distribution. J. Glob. Optim. 51 (2011) 189–208. [CrossRef] [Google Scholar]
  • D. Wang, C. Qi and H. Wang, Improving emergency response collaboration and resource allocation by task network mapping and analysis. Saf. Sci. 70 (2014) 9–18. [CrossRef] [Google Scholar]
  • J.-B. Wang and J.-J. Wang, Single machine scheduling with sum-of-logarithm-processing-times based and position based learning effects. Optim. Lett. 8 (2014) 971–982. [CrossRef] [MathSciNet] [Google Scholar]
  • Z. Wen, Z. Xiong, H. Lu and Y. Xia, Optimisation of treatment scheme for water inrush disaster in tunnels based on fuzzy multi-criteria decision-making in an uncertain environment. Arab. J. Sci. Eng. 44 (2019) 8249–8263. [CrossRef] [Google Scholar]
  • F. Wex, G. Schryen and D. Neumann, Intelligent decision support for centralized coordination during emergency response. In: Proceedings of the 8th International ISCRAM Conference (2011). [Google Scholar]
  • F. Wex, G. Schryen and D. Neumann, Operational emergency response under informational uncertainty: A fuzzy optimization model for scheduling and allocating rescue units, edited by Z. Franco and J.R.L. Rothkrantz, in: Proc. of the 9th International ISCRAM Conference. Simon Fraser University, Vancouver, BC (2012). [Google Scholar]
  • F. Wex, G. Schryen and D. Neumann, Decision modeling for assignments of collaborative rescue units during emergency response. In: 46th Hawaii International Conference on System Sciences. IEEE (2013) 166–175. [Google Scholar]
  • F. Wex, G. Schryen, S. Feuerriegel and D. Neumann, Emergency response in natural disaster management: Allocation and scheduling of rescue units. Eur. J. Oper. Res. 235 (2014) 697–708. [CrossRef] [Google Scholar]
  • N. Xu, Q. Zhang, H. Zhang, M. Hong, R. Akerkar and Y. Liang, Global optimization for multi-stage construction of rescue units in disaster response. Sustain. Cities Soc. 51 (2019) 101768. [CrossRef] [Google Scholar]
  • Y. Yin, C.-C. Wu, W.-H. Wu and S.-R. Cheng, The single-machine total weighted tardiness scheduling problem with position-based learning effects. Comput. Oper. Res. 39 (2012) 1109–1116. [CrossRef] [MathSciNet] [Google Scholar]
  • Y. Yuan, Z. Fan and Y. Liu, Study on the model for the assignment of rescue workers in emergency rescue. Chinese J. Manag. Sci. 21 (2013) 152–160. [Google Scholar]
  • M.H.F. Zarandi and V. Kayvanfar, A bi-objective identical parallel machine scheduling problem with controllable processing times: A just-in-time approach. Int. J. Adv. Manuf. Technol. 77 (2015) 545–563. [CrossRef] [Google Scholar]
  • C. Zhang, X. Liu, Y.P. Jiang, B. Fan and X. Song, A two-stage resource allocation model for lifeline systems quick response with vulnerability analysis. Eur. J. Oper. Res. 250 (2016) 855–864. [CrossRef] [Google Scholar]
  • S. Zhang, H. Guo, K. Zhu, S. Yu and J. Li, Multistage assignment optimization for emergency rescue teams in the disaster chain. Knowledge-Based Syst. 137 (2017) 123–137. [CrossRef] [Google Scholar]
  • L. Zhou, X. Wu, Z. Xu and H. Fujita, Emergency decision making for natural disasters: An overview. Int. J. Disaster Risk Reduct. 27 (2018) 567–576. [CrossRef] [Google Scholar]
  • E. Zitzler, Evolutionary algorithms for multiobjective optimization: Methods and applications (1999). [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.