Humanitarian aid delivery decisions during the early recovery phase of disaster using a discrete choice multi-attribute value method

  • R. K. JanaEmail author
  • Chandra Prakash Chandra
  • Aviral Kumar Tiwari
S.I. : Applications of OR in Disaster Relief Operations, Part II


The humanitarian aid delivery problem associated with the early recovery phase of a disaster often incorporates multiple attributes. In this paper, the relative importance of various humanitarian aid attributes was measured using a discrete choice multi-attribute value method. This approach identifies all possible non-dominated pairs explicitly ranked by experts and provides an overall complete ranking of attributes. The performance score of each aid delivery plan was then calculated using the attributes’ ranking by solving a corresponding linear programming model. As an application study, the issues pertaining to the early recovery phase of 2017 flood in Assam, India, were analyzed. It was concluded that the ‘delivery amount’ is the most preferred attribute selected by humanitarian experts.


Humanitarian logistics Aid delivery Early recovery phase Pairwise comparison Discrete choice method 



The authors would like to thank the Associate Editor and anonymous reviewers for their constructive comments that helped in improving the quality and presentation of this manuscript. The authors are also thankful to Dr. Shibu K. Mani, Tata Institute of Social Sciences, Mumbai Campus, for his support while conducting this study, and Professor Paul Hansen, Department of Economics, University of Otago, for granting free access to 1000minds software.


  1. Baron, J. (1997). Biases in the quantitative measurement of values for public decisions. Psychological Bulletin, 122(1), 72–88. Scholar
  2. Centre for Research on the Epidemiology of Disasters–CRED. (2016). Emergency events database (EM-DAT). Accessed 10 Nov 2017.
  3. Charter, H., & Response, D. (2011). The sphere project. Response (Vol. 1). ISBN 978-1-908176-00-4.Google Scholar
  4. Christoplos, I. (2006). Links between relief, rehabilitation and development in the tsunami response: A synthesis of initial findings. Joint Evaluation of the Tsunami Evaluation Coalition, 5, 1–115.Google Scholar
  5. De la Torre, L. E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-Economic Planning Sciences. Scholar
  6. Dubey, R., & Altay, N. (2018). Drivers of coordination in humanitarian relief supply chains. In G. Kovács, K. Spens, & M. Moshtari (Eds.), The Palgrave handbook of humanitarian logistics and supply chain management (pp. 297–325). London: Palgrave Macmillan.CrossRefGoogle Scholar
  7. Dubey, R., & Gunasekaran, A. (2016). The sustainable humanitarian supply chain design: Agility, adaptability and alignment. International Journal of Logistics Research and Applications, 19(1), 62–82. Scholar
  8. Etkin, D. (2016). An interdisciplinary approach to concepts and causes. Disaster Theory. Scholar
  9. Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety Science, 35, 41–57. Scholar
  10. Gad-el-Hak, M. (2008). Large-scale disasters: Prediction, control, and mitigation. Cambridge: Cambridge University Press. ISBN 9780521872.CrossRefGoogle Scholar
  11. Gralla, E., Goentzel, J., & Fine, C. (2014). Assessing trade-offs among multiple objectives for humanitarian aid delivery using expert preferences. Production and Operations Management, 23(6), 978–989. Scholar
  12. Green, P., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. Scholar
  13. Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351–366. Scholar
  14. Haghani, A., & Oh, S. C. (1996). Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations. Transportation Research Part A: Policy and Practice, 30(3), 231–250. Scholar
  15. Hansen, P., & Ombler, F. (2008). A new method for scoring additive multi-attribute value models using pairwise rankings of alternatives. Journal of Multi-Criteria Decision Analysis, 15(3–4), 87–107. Scholar
  16. Holguin-Veras, J., Taniguchi, E., Jaller, M., Aros-Vera, F., Ferreira, F., & Thompson, R. G. (2014). The Tohoku disasters: Chief lessons concerning the post disaster humanitarian logistics response and policy implications. Transportation Research Part A: Policy and Practice, 69, 86–104. Scholar
  17. Hu, C. L., Liu, X., & Hua, Y. K. (2016). A bi-objective robust model for emergency resource allocation under uncertainty. International Journal of Production Research, 54(24), 7421–7438. Scholar
  18. Huang, K., Jiang, Y., Yuan, Y., & Zhao, L. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review. Scholar
  19. Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation Research Part E: Logistics and Transportation Review, 48(1), 2–18. Scholar
  20. Huang, X., & Song, L. (2016). An emergency logistics distribution routing model for unexpected events. Annals of Operations Research. Scholar
  21. Humanitarian, T., & Group, P. (2003). HPG report. Security, 10(20), 34.Google Scholar
  22. Inter-Agency Group Assam. (2017). Joint needs assessment report.Google Scholar
  23. Jacobson, E. U., Argon, N. T., & Ziya, S. (2012). Priority assignment in emergency response. Operations Research, 60(4), 813–832. Scholar
  24. Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives–preferences and value tradeoffs. Behavioral Science. Scholar
  25. Kovács, G., & Spens, K. M. (2012). Relief supply chain management for disasters: Humanitarian aid and emergency logistics. Humanitarian Aid and Relief Supply Chain Management for Disasters. Scholar
  26. Lin, Y. H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio-Economic Planning Sciences, 45(4), 132–145. Scholar
  27. Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126(1), 76–84. Scholar
  28. Mitchell, R. C., & Carson, R. T. (2013). Using surveys to value public goods: The contingent valuation method. Rff Press.Google Scholar
  29. Mosel, I., & Levine, S. (2014). Remaking the case for linking relief, rehabilitation and development: How LRRD can become a practically useful concept for assistance in difficult places. HPG Commissioned Report (p. 27).Google Scholar
  30. Nolz, P. C., Doerner, K. F., Gutjahr, W. J., & Hartl, R. F. (2010). A bi-objective metaheuristic for disaster relief operation planning. Studies in Computational Intelligence, 272, 167–187. Scholar
  31. Ortuño, M. T., Cristóbal, P., Ferrer, J. M., Martín-Campo, F. J., Muñoz, S., Tirado, G., et al. (2013). Decision aid models and systems for humanitarian logistics. A survey. In B. Vitoriano, J. Montero, & D. Ruan (Eds.), Decision aid models for disaster management and emergencies (Vol. 7, pp. 17–44). Berlin: Springer. Scholar
  32. Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research, 129(1–4), 217–245. Scholar
  33. Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location-routing problem in disaster relief. Computers and Operations Research, 42, 25–39. Scholar
  34. Sudman, S., Mitchell, R. C., & Carson, R. T. (1991). Using surveys to value public goods: The contingent valuation method. Contemporary Sociology, 20(2), 243. Scholar
  35. Thomas, A. S., & Mizushima, M. (2005). Logistics training: Necessity or luxury? Forced Migration Review, 22, 60–61.Google Scholar
  36. Tomasini, R. M., & van Wassenhove, L. (2009). Humanitarian logistics (Vol. 38, pp. 178). INSEAD Business Press, TS-hbz Hochschulbibliothekszentrum NR. Scholar
  37. Tzeng, G.-H., Cheng, H.-J., & Huang, T. D. (2007). Multi-objective optimal planning for designing relief delivery systems. Transportation Research Part E: Logistics and Transportation Review, 43(6), 673–686. Scholar
  38. United Nations University. (2016). World risk report 2016—Logistics and infrastructure. World Risk Report, 74. ISBN 9783946785026.Google Scholar
  39. Urrea, G., Villa, S., & Gonçalves, P. (2016). Exploratory analyses of relief and development operations using social networks. Socio-Economic Planning Sciences, 56, 27–39. Scholar
  40. Wang, X., Wu, Y., Liang, L., & Huang, Z. (2016). Service outsourcing and disaster response methods in a relief supply chain. Annals of Operations Research, 240(2), 471–487. Scholar
  41. Wash Information Management Toolkit. (2014). Accessed 10 Nov 2017.
  42. Weinstein, M. C., Torrance, G., & McGuire, A. (2009). QALYs: The basics. Value in health. Scholar
  43. Whitehead, S. J., & Ali, S. (2010). Health outcomes in economic evaluation: The QALY and utilities. British Medical Bulletin. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of Management RaipurRaipurIndia
  2. 2.Montpellier Business SchoolMontpellierFrance

Personalised recommendations