Excellence in Research and Innovation for Humanity

International Science Index


Select areas to restrict search in scientific publication database:
10006027
A Reinforcement Learning Approach for Evaluation of Real-Time Disaster Relief Demand and Network Condition
Abstract:
Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.
Digital Article Identifier (DAI):

References:

[1] Liu, W., G. Hu, and J. Li, Emergency resources demand prediction using case-based reasoning. Safety Science, 2012. 50(3): p. 530-534.
[2] Aviv, Y., A time-series framework for supply-chain inventory management. Operations Research, 2003. 51(2): p. 210-227.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] Fiedrich, F., F. Gehbauer, and U. Rickers, Optimized resource allocation for emergency response after earthquake disasters. Safety science, 2000. 35(1): p. 41-57.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] Özdamar, L., E. Ekinci, and B. Küçükyazici, Emergency logistics planning in natural disasters. Annals of operations research, 2004. 129(1-4): p. 217-245.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] Watkins, C.J. and P. Dayan, Q-learning. Machine learning, 1992. 8(3-4): p. 279-292.
[21] Melo, F.S., Convergence of Q-learning: A simple proof. Institute Of Systems and Robotics, Tech. Rep, 2001.
[22] Isfahan Atlas, 2013 (Accessed: August, 2016).
Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007