A Reinforcement Learning Approach for Evaluation of Real-Time Disaster Relief Demand and Network Condition
 Liu, W., G. Hu, and J. Li, Emergency resources demand prediction using case-based reasoning. Safety Science, 2012. 50(3): p. 530-534.
 Aviv, Y., A time-series framework for supply-chain inventory management. Operations Research, 2003. 51(2): p. 210-227.
 Sun, B., W. Ma, and H. Zhao, A fuzzy rough set approach to emergency material demand prediction over two universes. Applied Mathematical Modelling, 2013. 37(10): p. 7062-7070.
 Sheu, J.-B., Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 2010. 46(1): p. 1-17.
 Huang, M., K.R. Smilowitz, and B. Balcik, A continuous approximation approach for assessment routing in disaster relief. Transportation Research Part B: Methodological, 2013. 50: p. 20-41.
 Edrissi, A., M. Nourinejad, and M.J. Roorda, Transportation network reliability in emergency response. Transportation research part E: logistics and transportation review, 2015. 80: p. 56-73.
 Fiedrich, F., F. Gehbauer, and U. Rickers, Optimized resource allocation for emergency response after earthquake disasters. Safety science, 2000. 35(1): p. 41-57.
 Rennemo, S.J., et al., A three-stage stochastic facility routing model for disaster response planning. Transportation research part E: logistics and transportation review, 2014. 62: p. 116-135.
 Cavdur, F., M. Kose-Kucuk, and A. Sebatli, Allocation of temporary disaster response facilities under demand uncertainty: An earthquake case study. International Journal of Disaster Risk Reduction, 2016. 19: p. 159-166.
 Chen, L. and E. Miller-Hooks, Optimal team deployment in urban search and rescue. Transportation Research Part B: Methodological, 2012. 46(8): p. 984-999.
 Wohlgemuth, S., R. Oloruntoba, and U. Clausen, Dynamic vehicle routing with anticipation in disaster relief. Socio-Economic Planning Sciences, 2012. 46(4): p. 261-271.
 Özdamar, L., E. Ekinci, and B. Küçükyazici, Emergency logistics planning in natural disasters. Annals of operations research, 2004. 129(1-4): p. 217-245.
 Luis, E., I.S. Dolinskaya, and K.R. Smilowitz, Disaster relief routing: Integrating research and practice. Socio-economic planning sciences, 2012. 46(1): p. 88-97.
 Barbarosoǧlu, G. and Y. Arda, A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the operational research society, 2004. 55(1): p. 43-53.
 Özdamar, L. and O. Demir, A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transportation Research Part E: Logistics and Transportation Review, 2012. 48(3): p. 591-602.
 Yi, W. and A. Kumar, Ant colony optimization for disaster relief operations. Transportation Research Part E: Logistics and Transportation Review, 2007. 43(6): p. 660-672.
 Ahmadi, M., A. Seifi, and B. Tootooni, 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: Logistics and Transportation Review, 2015. 75: p. 145-163.
 Najafi, M., K. Eshghi, and W. Dullaert, A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review, 2013. 49(1): p. 217-249.
 Huang, K., et al., Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 2015. 75: p. 1-17.
 Watkins, C.J. and P. Dayan, Q-learning. Machine learning, 1992. 8(3-4): p. 279-292.
 Melo, F.S., Convergence of Q-learning: A simple proof. Institute Of Systems and Robotics, Tech. Rep, 2001.
 Isfahan Atlas, 2013 (Accessed: August, 2016).