Document Type : Review Paper

Authors

1 Department of Industrial Engineering, Yazd University, Yazd, Iran.

2 Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran.

Abstract

Iran is among the ten disaster-prone countries and, in terms of the earthquake, it has been ranked the sixth in the world. Although the damages caused by the disasters are not irreversible from different aspects, they could be minimized by performing appropriate preventions as well as preparing plans to counteract the impacts of such incidents. According to the increasing trend of disasters and crises, which hardly damage businesses and communities, providing relief supply chain for crisis situations (HDRSC) is necessary and vital in the wide field of supply chain management (SCM). Even though considerable attempts have been performed in the field of supply chain focusing on the disaster relief, few studies have addressed its complex features and properties. This paper concentrates on the activities such as demand determination and supply chain coordination by field study and best review of best-related research. This research provides a framework for supply chain managers in crisis, who face similar problems in other environments, with valuable insights.

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Main Subjects

[1] Alinaghian, M., Goli, A., & Mokhatab, R. F. (2015). Disaster relief vehicle routing with covering approach and fuzzy demand using hybrid harmony search algorithm. JOURNAL OF INDUSTRIAL ENGINEERING. 49(1), 79-92.
[2] Beamon, B. M. (2004, November). Humanitarian relief chains: issues and challenges. In Proceedings of the 34th International Conference on Computers and Industrial Engineering (Vol. 34, pp. 77-82). Seattle, WA: University of Washington.
[3] Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR spectrum, 35(4), 905- 933.
[4] Chang, M. S., Tseng, Y. L., & Chen, J. W. (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 43(6), 737-754.
[5] Clark, A., & Culkin, B. (2013). A network transshipment model for planning humanitarian relief operations after a natural disaster. Decision Aid Models for Disaster Management and Emergencies, Atlantis Computational Intelligence Systems, 7, 233-257.
[6] De Angelis, V., Mecoli, M., Nikoi, C., & Storchi, G. (2007). Multiperiod integrated routing and scheduling of World Food Programme cargo planes in Angola. Computers & Operations Research, 34(6), 1601-1615.
[7] Ganjali, M., Shirouyehzad, H., & Shahin, A. (2016). Applied Research on Industrial Engineering. Journal of Applied Research on Industrial Engineering, 3(1), 39-48.
[8] Goli, A., & Alinaghian, M. (2015). Location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling. Decision Science Letters, 4(4), 579-592.
[9] Huang, H. H., Hsu, Y. T., & Miralinaghi, M. (2017). A Location Problem of Two-Level Disaster Relief Facilities for Vulnerable Networks (No. 17-06756).
[10] Jang, H. C., Lien, Y. N., & Tsai, T. C. (2009, June). Rescue information system for earthquake disasters based on MANET emergency communication platform. In Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly (pp. 623-627). ACM. [11] 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. [12] Salimi, A., Tavakoli Moghadam, R., & Bashiri, M. (2016). Designing Green Reverse Logistics Network for Recycling of Solid Waste under Conditions of Uncertainty. Journal of Applied Research on Industrial Engineering, 3(1), 15-29.
[13] Taylor, C. C. S., & Arthanari, T. (2017). Enabling Disaster Relief Supply Chain Visibility (SCV) and Supply Chain Coordination (SCC).
[14] 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.
[15] Van Hentenryck, P., Bent, R., & Coffrin, C. (2010, June). Strategic Planning for Disaster Recovery with Stochastic Last Mile Distribution. In CPAIOR (Vol. 6140, pp. 318-333).
[16] Weisz, A., & Taubman, A. (2017). Emerging Concerns for International Social Work and Disaster Response: From Relief to Development and Sustainability. Columbia Social Work Review, (2011).
[17] Yi, W., & Özdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research, 179(3), 1177-1193.
[18] Nolz, P. C., Doerner, K. F., Gutjahr, W. J., & Hartl, R. F. (2010). A Bi-objective Metaheuristic for Disaster Relief Operation Planning. Advances in multi-objective nature inspired computing, 272, 167- 187.
[19] Duran, S., Gutierrez, M. A., & Keskinocak, P. (2011). Pre-positioning of emergency items for CARE international. Interfaces, 41(3), 223-237.