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Annals of Operations Research

, Volume 283, Issue 1–2, pp 1–8 | Cite as

Disaster relief operations: past, present and future

  • Rameshwar DubeyEmail author
  • Angappa Gunasekaran
  • Thanos Papadopoulos
SI: Applications of OR in Disaster Relief Operations

Abstract

The aim of the preface is to introduce the scope of this special issue (SI). We explain our editorial approach and summarise our findings based on articles included in this SI. Finally, we outline future research questions which stemmed out of the discussions of this SI.

Keywords

Disaster relief operations Humanitarian operations Humanitarian supply chain management 

Notes

Acknowledgements

We are extremely grateful to Annals of Operations Research Editor-in-Chief, Professor Endre Boros, for his immense support and guidance throughout the review process. We are equally grateful to Ms. Katie D’Agosta for her continuous support throughout the review process. We are also grateful to Ms. Ann Pulido for her timely support which helped us conclude this big project. We are also equally grateful to our reviewers who have played a significant role in this project. Finally, we are thankful to Professor David Roubaud and Montpellier Business School team for their excellent support that enabled us to accomplish this project.

References

  1. Akter, S., & Fosso Wamba, S. (2017). Big data and disaster management: A systematic review and agenda for future research. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2584-2.CrossRefGoogle Scholar
  2. Altay, N. (2008). Issues in disaster relief logistics. In M. Gad-el-Hak (Ed.), Large-scale disasters: Prediction, control and mitigation. Cambridge: Cambridge University Press.Google Scholar
  3. Altay, N., & Green, W. G., III. (2006). OR/MS research in disaster operations management. European Journal of Operational Research,175(1), 475–493.CrossRefGoogle Scholar
  4. Altay, N., Gunasekaran, A., Dubey, R., & Childe, S. J. (2018). Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: A dynamic capability view. Production Planning and Control,29(14), 1158–1174.CrossRefGoogle Scholar
  5. Anparasan, A., & Lejeune, M. (2017). Resource deployment and donation allocation for epidemic outbreaks. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2392-0.CrossRefGoogle Scholar
  6. Baharmand, H., Comes, T., & Lauras, M. (2017). Defining and measuring the network flexibility of humanitarian supply chains: Insights from the 2015 Nepal earthquake. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2713-y.CrossRefGoogle Scholar
  7. Baidya, A., & Bera, U. K. (2018). New model for addressing supply chain and transport safety for disaster relief operations. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2765-7.CrossRefGoogle Scholar
  8. Banomyong, R., Varadejsatitwong, P., & Oloruntoba, R. (2017). A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2549-5.CrossRefGoogle Scholar
  9. Bao, S., Zhang, C., Ouyang, M., & Miao, L. (2017). An integrated tri-level model for enhancing the resilience of facilities against intentional attacks. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2705-y.CrossRefGoogle Scholar
  10. Behl, A., & Dutta, P. (2018). Humanitarian supply chain management: A thematic literature review and future directions of research. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2806-2.CrossRefGoogle Scholar
  11. Besiou, M., Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2018). OR applied to humanitarian operations. European Journal of Operational Research,269(2), 397–405.CrossRefGoogle Scholar
  12. Çankaya, E., Ekici, A., & Özener, O. Ö. (2018). Humanitarian relief supplies distribution: An application of inventory routing problem. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2781-7.CrossRefGoogle Scholar
  13. Chakravarty, A. (2014). Humanitarian relief chain: Rapid response under uncertainty. International Journal of Production Economics,151, 146–157.CrossRefGoogle Scholar
  14. de Camargo, J. A., Mendonça, P. S. M., de Oliveira, J. H. C., Jabbour, C. J. C., & de Sousa Jabbour, A. B. L. (2017). Giving voice to the silent: A framework for understanding stakeholders’ participation in socially-oriented initiatives, community-based actions and humanitarian operations projects. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2426-2.CrossRefGoogle Scholar
  15. de Mattos, R. G., Oliveira, F., Leiras, A., de Paula Filho, A. B., & Gonçalves, P. (2018). Robust optimization of the insecticide-treated bed nets procurement and distribution planning under uncertainty for malaria prevention and control. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3015-8.CrossRefGoogle Scholar
  16. Dubey, R., Altay, N., & Blome, C. (2017). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2676-z.CrossRefGoogle Scholar
  17. Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., et al. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics,210, 120–136.CrossRefGoogle Scholar
  18. DuHadway, S., Carnovale, S., & Hazen, B. (2017). Understanding risk management for intentional supply chain disruptions: Risk detection, risk mitigation, and risk recovery. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2452-0.CrossRefGoogle Scholar
  19. Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2681-2.CrossRefGoogle Scholar
  20. Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2017). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2729-3.CrossRefGoogle Scholar
  21. Fok, D., van Stel, A., Burke, A., & Thurik, R. (2019). How entry crowds and grows markets: The gradual disaster management view of market dynamics in the retail industry. Annals of Operations Research.  https://doi.org/10.1007/s10479-019-03322-y.CrossRefGoogle Scholar
  22. Fosso Wamba, S., Edwards, A., & Akter, S. (2017). Social media adoption and use for improved emergency services operations: The case of the NSW SES. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2545-9.CrossRefGoogle Scholar
  23. Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research,230(2), 201–211.CrossRefGoogle Scholar
  24. Goldschmidt, K. H., & Kumar, S. (2017). Reducing the cost of humanitarian operations through disaster preparation and preparedness. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2587-z.CrossRefGoogle Scholar
  25. Griffith, D. A., Boehmke, B., Bradley, R. V., Hazen, B. T., & Johnson, A. W. (2017). Embedded analytics: Improving decision support for humanitarian logistics operations. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2607-z.CrossRefGoogle Scholar
  26. Gunasekaran, A., Dubey, R., Wamba, S. F., Papadopoulos, T., Hazen, B. T., & Ngai, E. W. T. (2018). Bridging humanitarian operations management and organisational theory. International Journal of Production Research,56(21), 6735–6740.CrossRefGoogle Scholar
  27. Gupta, S., Altay, N., & Luo, Z. (2017). Big data in humanitarian supply chain management: A review and further research directions. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2671-4.CrossRefGoogle Scholar
  28. Han, S., Huang, H., Luo, Z., & Foropon, C. (2018). Harnessing the power of crowdsourcing and internet of things in disaster response. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2884-1.CrossRefGoogle Scholar
  29. Holguín-Veras, J., Jaller, M., Van Wassenhove, L. N., Pérez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management,30(7–8), 494–506.CrossRefGoogle Scholar
  30. Hoskins, A. B., & Medal, H. R. (2018). Stochastic programming solution for placement of satellite ground stations. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2798-y.CrossRefGoogle Scholar
  31. Ivanov, D., & Sokolov, B. (2019). Simultaneous structural–operational control of supply chain dynamics and resilience. Annals of Operations Research.  https://doi.org/10.1007/s10479-019-03231-0.CrossRefGoogle Scholar
  32. Jabbour, C. J. C., Sobreiro, V. A., de Sousa Jabbour, A. B. L., de Souza Campos, L. M., Mariano, E. B., & Renwick, D. W. S. (2017). An analysis of the literature on humanitarian logistics and supply chain management: Paving the way for future studies. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2536-x.CrossRefGoogle Scholar
  33. Jana, R. K., Chandra, C. P., & Tiwari, A. K. (2018). Humanitarian aid delivery decisions during the early recovery phase of disaster using a discrete choice multi-attribute value method. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3074-x.CrossRefGoogle Scholar
  34. John, L., Gurumurthy, A., Soni, G., & Jain, V. (2018). Modelling the inter-relationship between factors affecting coordination in a humanitarian supply chain: A case of Chennai flood relief. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2963-3.CrossRefGoogle Scholar
  35. Kaur, H., & Singh, S. P. (2016). Sustainable procurement and logistics for disaster resilient supply chain. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2374-2.CrossRefGoogle Scholar
  36. Khalilpourazari, S., & Khamseh, A. A. (2017). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: A comprehensive study with real world application. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2588-y.CrossRefGoogle Scholar
  37. Kilmann, R. H., & Mitroff, I. I. (1976). Qualitative versus quantitative analysis for management science: Different forms for different psychological types. Interfaces,6(2), 17–27.CrossRefGoogle Scholar
  38. Kim, D., Lee, K., & Moon, I. (2018a). Stochastic facility location model for drones considering uncertain flight distance. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3114-6.CrossRefGoogle Scholar
  39. Kim, S., Ramkumar, M., & Subramanian, N. (2018b). Logistics service provider selection for disaster preparation: A socio-technical systems perspective. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-03129-3.CrossRefGoogle Scholar
  40. Kovács, G., & Spens, K. M. (2011). Trends and developments in humanitarian logistics-a gap analysis. International Journal of Physical Distribution & Logistics Management,41(1), 32–45.CrossRefGoogle Scholar
  41. Laguna-Salvadó, L., Lauras, M., Okongwu, U., & Comes, T. (2018). A multicriteria master planning DSS for a sustainable humanitarian supply chain. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2882-3.CrossRefGoogle Scholar
  42. Leigh, J., Dunnett, S., & Jackson, L. (2017). Predictive police patrolling to target hotspots and cover response demand. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2528-x.CrossRefGoogle Scholar
  43. Li, S., & Teo, K. L. (2018). Post-disaster multi-period road network repair: Work scheduling and relief logistics optimization. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3037-2.CrossRefGoogle Scholar
  44. Lodree, E. J., Altay, N., & Cook, R. A. (2017). Staff assignment policies for a mass casualty event queuing network. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2635-8.CrossRefGoogle Scholar
  45. Malekpoor, H., Chalvatzis, K., Mishra, N., & Ramudhin, A. (2018). A hybrid approach of VIKOR and bi-objective integer linear programming for electrification planning in a disaster relief camp. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2877-0.CrossRefGoogle Scholar
  46. Mediouni, A., Zufferey, N., Subramanian, N., & Cheikhrouhou, N. (2018). Fit between humanitarian professionals and project requirements: Hybrid group decision procedure to reduce uncertainty in decision-making. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2782-6.CrossRefGoogle Scholar
  47. Mishra, D., Kumar, S., & Hassini, E. (2018). Current trends in disaster management simulation modelling research. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2985-x.CrossRefGoogle Scholar
  48. Nilsang, S., Yuangyai, C., Cheng, C. Y., & Janjarassuk, U. (2018). Locating an ambulance base by using social media: A case study in Bangkok. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2918-8.CrossRefGoogle Scholar
  49. Olaogbebikan, J. E., & Oloruntoba, R. (2017). Similarities between disaster supply chains and commercial supply chains: A SCM process view. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2690-1.CrossRefGoogle Scholar
  50. Oloruntoba, R., Hossain, G. F., & Wagner, B. (2016). Theory in humanitarian operations research. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2378-y.CrossRefGoogle Scholar
  51. Prasad, S., Woldt, J., Tata, J., & Altay, N. (2017). Application of project management to disaster resilience. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2679-9.CrossRefGoogle Scholar
  52. Pyakurel, U., Nath, H. N., & Dhamala, T. N. (2018). Partial contraflow with path reversals for evacuation planning. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3031-8.CrossRefGoogle Scholar
  53. Rahmani, D. (2018). Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2960-6.CrossRefGoogle Scholar
  54. Sabouhi, F., Bozorgi-Amiri, A., Moshref-Javadi, M., & Heydari, M. (2018). An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: A case study. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2807-1.CrossRefGoogle Scholar
  55. Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2017). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2533-0.CrossRefGoogle Scholar
  56. Samani, M. R. G., & Hosseini-Motlagh, S. M. (2018). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2873-4.CrossRefGoogle Scholar
  57. Shareef, M. A., Dwivedi, Y. K., Mahmud, R., Wright, A., Rahman, M. M., Kizgin, H., et al. (2018). Disaster management in Bangladesh: Developing an effective emergency supply chain network. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3081-y.CrossRefGoogle Scholar
  58. Sharma, B., Ramkumar, M., Subramanian, N., & Malhotra, B. (2017). Dynamic temporary blood facility location-allocation during and post-disaster periods. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2680-3.CrossRefGoogle Scholar
  59. Singh, J. P., Dwivedi, Y. K., Rana, N. P., Kumar, A., & Kapoor, K. K. (2017). Event classification and location prediction from tweets during disasters. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2522-3.CrossRefGoogle Scholar
  60. Sinha, A., Kumar, P., Rana, N. P., Islam, R., & Dwivedi, Y. K. (2017). Impact of internet of things (IoT) in disaster management: A task-technology fit perspective. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2658-1.CrossRefGoogle Scholar
  61. Song, M., & Du, Q. (2017). Analysis and exploration of damage-reduction measures for flood disasters in China. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2589-x.CrossRefGoogle Scholar
  62. Sushil, (2017a). Efficient interpretive ranking process incorporating implicit and transitive dominance relationships. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2608-y.CrossRefGoogle Scholar
  63. Sushil, (2017b). Theory building using SAP-LAP linkages: An application in the context of disaster management. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2425-3.CrossRefGoogle Scholar
  64. Tayal, A., & Singh, S. P. (2017). Formulating multi-objective stochastic dynamic facility layout problem for disaster relief. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2592-2.CrossRefGoogle Scholar
  65. Turkeš, R., Cuervo, D. P., & Sörensen, K. (2017). Pre-positioning of emergency supplies: Does putting a price on human life help to save lives? Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2702-1.CrossRefGoogle Scholar
  66. Venkatesh, V. G., Zhang, A., Deakins, E., Luthra, S., & Mangla, S. (2018). A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2981-1.CrossRefGoogle Scholar
  67. Yahyaei, M., & Bozorgi-Amiri, A. (2018). Robust reliable humanitarian relief network design: An integration of shelter and supply facility location. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-2758-6.CrossRefGoogle Scholar
  68. Yang, Z., Guo, L., & Yang, Z. (2017). Emergency logistics for wildfire suppression based on forecasted disaster evolution. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2598-9.CrossRefGoogle Scholar
  69. Zhang, J., Wang, Z., & Ren, F. (2019). Optimization of humanitarian relief supply chain reliability: A case study of the Ya’an earthquake. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-03127-5.CrossRefGoogle Scholar
  70. Zhu, L., Gong, Y., Xu, Y., & Gu, J. (2018). Emergency relief routing models for injured victims considering equity and priority. Annals of Operations Research.  https://doi.org/10.1007/s10479-018-3089-3.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Rameshwar Dubey
    • 1
    Email author
  • Angappa Gunasekaran
    • 2
  • Thanos Papadopoulos
    • 3
  1. 1.Montpellier Business SchoolMontpellier Research in ManagementMontpellierFrance
  2. 2.School of Business and Public AdministrationCalifornia State UniversityBakersfieldUSA
  3. 3.Kent Business SchoolUniversity of KentKentUK

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