A humanitarian reconfiguration and rehabilitation model for preparedness and response to earthquakes using a scheduled reopening of links

Document Type : Research Paper


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

2 Department of Industrial Engineering, Shahed University, Tehran, Iran

3 School of Science, RMIT University, Melbourne, Australia


This study proposes a novel mathematical model for redesigning existing relief logistics network including suppliers, distribution centers and demand nodes along with integrating the measures in preparedness and response phases, simultaneously. In order to improve the accessibility and connectivity, certain precautionary measures for strengthening and rehabilitation of the links have been taken into account in the preparedness phase. In addition, a new debris clearance scheduling model for blocked links is modeled in accordance with the rehabilitation strategies. To overcome the uncertainty in a predefined destruction scenario tree, a multi-stage stochastic programming has been applied in a real case study. The results obtained in the proposed model indicate that the redesigned network leads to better performance in dealing with evacuees’ requested relief as compared to the results obtained by the existing network. Moreover, the results clearly demonstrate the significant value of solutions determined by multi-stage stochastic programming.


Main Subjects

Afshar, A. and Haghani, A., (2012). Modeling integrated supply chain logistics in real-time large–scale disaster relief operations, Socio-Economic Planning Science 46, 327-338.
Ahmadi, M., Seifi, A., and Tootooni, B., (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: a case study on San Francisco district. Transportation Research Part E 75, 145-163.
Ahmed, S., King, A. J., and Parja, G., (2003). A multi-stage stochastic integer programming approach for capacity expansion under uncertainty. Journal of Global Optimization 26(1), 3-24.
Aksu, D. T. and Ozdamar, L., (2014). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E: Logistics and Transportation Review 61, 56-67.
Al Theeb, N. and Murray, C., (2017), Vehicle routing and resource distribution in post disaster humanitarian relief operations. Intl. Trans. in Op. Res. 24, 1253–1284.
Boonmee, C., Arimura. M., and Asada, T., (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, DOI: 10.1016/j.ijdrr.2017.01.017.
Bozorgi-Amiri, A. and Asvadi, S., (2015). A prioritization model for locating relief logistic centers using analytic hierarchy process with interval comparison matrix. Knowledge-Based Systems 86, 173-181.
Cavdaroglu, B., Hammel, E., Mitchell, J. E., Sharkey, T. C. and Wallace, W. A., (2013). Integrating restoration and scheduling decisions for disrupted interdependent infrastructure systems. Annals of Operations Research203(1), 279-294.
Chen, Y., Tzeng, G., (2000). Determining the optimal reconstruction priority of a post-quake road network. Computing in Civil and Building Engineering 686– 693.
Condexia, L., Leiras, A., Oliveria F. and Brito, I., (2017). Disaster relief supply pre-positioning optimization: a risk analysis via shortage mitigation. International Journal of Disaster Risk Reduction, DOI:10.1016/j.ijdrr.2017.09.007.
Escudero, G. and Merino, P., (2007). The value of the stochastic solution in multistage problems, Top 15, 48–64.
Feng, C.M., Wang, T.C., (2003). Highway emergency rehabilitation scheduling in post-earthquake 72 hours. Journal of the Eastern Asia Society for Transportation Studies 5, 3276–3285.
Galindo, G. and Rajan. B., (2013). Pre-positioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies.Socio-Economic Planning Science 47, 20-37.
Gutjahr, W. and Nolz P. C., (2016). Multi-criteria optimization in humanitarian aid. European Journal of Operational Research, 252, 351–366.
Hobeika AG and Kim C., (1998). Comparison of traffic assignments in evacuation modeling. IEEE Transactions of Engineering Management 45(2), 192-198.
Hoyos, M. C., Morales, R. S. and Akhavan-Tabatabaei, R., (2015). OR models with stochastic components in disaster operations management: A literature survey. Computers & Industrial Engineering 82, 183-197.
Kunz, N., Reiner G. and Gold S., (2014). Investing in disaster management capabilities versus pre-positioning inventory: A new approach to disaster preparedness. International Journal of Production Economics 157, 261-272.
Melo, M.T., Nickle, S. and Saldanhada Gama, F., (2006). Dynamic Multi-Commodity Capacitated Facility Location: a Mathematical Modeling Framework for Strategic Supply Chain Planning. Computers & Operations Research 33, 181-208.
Nezir, A., (2012). Sampling based progressive hedging algorithms for stochastic programming problems, Wayne State University Dissertations, 528.  
Nickel, S., Saldaha-da-Gama, F. and Ziegler, H. P., (2012). A multi-stage stochastic supply network design problem with financial decisions and risk management. Omega 40, 511-524.
Noyan, N., (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers and Operations Research 39(3), 541-559. 
Peeta, S., Salman, S., Gunnec, D. and Viswanath, K., (2010). Pre-disaster investment decisions for strengthening a highway network. Computers & Operations research 37(10), 1708-1719.
Rath, S., Gendreau, M. and Gutjahr, W. J., (2016). Bi-objective stochastic programming models for determining depot locations in disaster relief operations. Intl. Trans. in Op. Res. 23, 997–1023.
Rawls, C. G. and Turnquist, M. A., (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological 44(4), 521 – 534.
Rawls, C. G. and Turnquist, M. A., (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster, Socio-Economic Planning Sciences 46(1), 46-54.
Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Cheikhrouhou, N. and Taheri-Moghaddam, A., (2016). An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation Research Part E: Logistics and Transportation Review 93, 485-509.
Rezaei-Malek, M., Tavakkoli-Moghadam, R., Zahiri, B. and Bozorgi-Amiri, Ali, (2016). An interactive approach for designing a robust disaster relief logistics network with perishable commodities, Computers & Operations Research 94, 201-215.
Rodrıguez-Espindola, O. and Gaytan, J., (2014). Scenario-based preparedness plan for floods. Natural Hazards 76(2), 1241-1262.
Salman, F. S. and Yucel, E., (2015). Emergency facility location under random network damage: Insights from the Istanbul case. Computers & Operations Research 62, 266-281.
Shuwen, Z., Haixiang, G., Kejun, Z., Shiwei, Y. and Jinling. L., (2017). Multistage Assignment Optimization for Emergency Rescue Teams in the Disaster Chain, Knowledge-Based Systems, DOI: 10.1016/j.knosys.2017.09.024.
Stepanov, A., and Smith, JM., (2009). Multi-objective evacuation routing in transportation networks, European Journal Operational Research 198(2), 435-446.
Vargas-Florez, J., Lauras, M., Okongwu, U. and Dupont, L., (2015). A decision support system for robust humanitarian facility location. Engineering Applications of Artificial Intelligence 46, 326-335.
Verma, A. and Gaukler, G. M., (2015). Pre-positioning disaster response facilities at safe locations: An evaluation of deterministic and stochastic modeling approaches. Computers & Operations Research 62, 197-209.
Yan, S. and Shih, Y.L., (2009). Optimal scheduling of emergency roadway repair and subsequent relief distribution. Computers & Operations Research 36 (6), 2049–2065.
Yan, S. and Shih, Y.L., (2012). An ant colony system-based hybrid algorithm for an emergency roadway repair time-space network flow problem, Transportmetrica 8 (5), 361–386.
Zolfaghari, M.R. and Peyghaleh, E., (2010). Probabilistic Earthquake Scenarios for the City of Tehran. Proceeding of 14-th European Conference of Earthquake Engineering.
Volume 11, Issue 4 - Serial Number 4
November 2018
Pages 96-115
  • Receive Date: 28 May 2018
  • Revise Date: 17 July 2018
  • Accept Date: 10 October 2018
  • First Publish Date: 25 November 2018