JSSM  Vol.8 No.1 , February 2015
Review of Relief Demand Forecasting Problem in Emergency Logistic System
Author(s) Jianan Zhao, Cejun Cao*
ABSTRACT
Demand forecasting on relief is the premise and basis of material allocation scheme in emergency logistic system. Reasonable demand forecasting method can facilitate relief distribution, thus avoiding the phenomenon that supply-demand imbalance and relief distribution delay. In this paper, relief will be categorized from point view of government and academia, to explain the relationship between relief categorization and demand forecasting. Then introduce the characteristics of relief-demand from several aspects, such as sudden, uncertainty, timeliness, and stage. Finally, this paper gives an overall conclusion on current development of relief demand forecasting method. And elaborate the application of case-based reasoning, information entropy theory, considering safety stock in the field of relief-demand forecasting in detail, to provide reference for relief distribution.

Cite this paper
Zhao, J. and Cao, C. (2015) Review of Relief Demand Forecasting Problem in Emergency Logistic System. Journal of Service Science and Management, 8, 92-98. doi: 10.4236/jssm.2015.81011.
References
[1]   Sun, B.Z., Ma, W.M. and Zhao, H.Y. (2013) A Fuzzy Rough Set Approach to Emergency Material Demand Prediction over Two Universes. Applied Mathematical Modelling, 37, 7062-7070.
http://dx.doi.org/10.1016/j.apm.2013.02.008

[2]   Wilson, D.T., Hawe, G.I., Coates, G. and Crouch, R.S. (2013) A Multi-Objective Combinatorial Model of Casualty Processing in Major Incident Response. European Journal of Operational Research, 230, 643-655.
http://dx.doi.org/10.1016/j.ejor.2013.04.040

[3]   Xu, X.Y., Qi, Y.Q. and Hua, Z.S. (2010) Forecasting Demand of Commodities after Natural Disasters. Expert Systems with Applications, 37, 4313-4317.
http://dx.doi.org/10.1016/j.eswa.2009.11.069

[4]   Jones, S.S., Evans, R.S., Allen, T.L., et al. (2009) A Multivariate Time Series Approach to Modeling and Forecasting Demand in the Emergency Department. Journal of Biomedical Informatics, 42, 123-139.
http://dx.doi.org/10.1016/j.jbi.2008.05.003

[5]   Ninno, C.D., Dorosh, P.A. and Smith, L.C. (2003) Public Policy, Markets and Household Coping Strategies in Bangladesh: Avoiding a Food Security Crisis Following the 1998 Flood. World Development, 31, 1221-1238.
http://dx.doi.org/10.1016/S0305-750X(03)00071-8

[6]   Liu, W.M., Hu, G.Y. and Li, J.F. (2012) Emergency Resources Demand Prediction Using Case-Based Reasoning. Safety Science, 50, 530-534.
http://dx.doi.org/10.1016/j.ssci.2011.11.007

[7]   Wang, W. and Liu, M. (2010) Method of Emergency Resources Demand Forecasting Based on Case-Based Reasoning. Journal of Safety and Environment, 10, 217-220.

[8]   Deng, S.C., Wu, Q., Shi, B., et al. (2014) Prediction of Resource for Responding Waterway Transportation Emergency Based on Case-Based Reasoning. China Safety Science Journal, 24, 79-84.

[9]   Wang, X. and Zhuang, Y. (2010) Forecasting Model of Unconventional Emergence Incident’s Resource Demand Based on Case-Based Reasoning. Journal of Xidian University (Social Science Edition), 20, 22-26.

[10]   Sheu, J.B. (2010) Dynamic Relief-Demand Management for Emergency Logistics Operations under Large-Scale Disasters. Transportation Research Part E: Logistics and Transportation Reviews, 46, 1-17.
http://dx.doi.org/10.1016/j.tre.2009.07.005

[11]   Sheu, J.B. (2007) An Emergency Logistics Distribution Approach for in Disasters. Transportation Research Part E: Logistics and Transportation Reviews, 43, 687-709.
http://dx.doi.org/10.1016/j.tre.2006.04.004

[12]   Fiedrich, F., Gehbauer, F. and Rickers, U. (2000) Optimized Resource Allocation for Emergency Response after Earthquake Disasters. Safety Science, 35, 41-57.

[13]   Qin, J.C., Xing, Y.T., Wang, S., et al. (2012) An Inter-Temporal Resource Emergency Management Model. Computers & Operations Research, 39, 1909-1918.
http://dx.doi.org/10.1016/j.cor.2011.07.008

[14]   Li, C.D., Cao, C.J. Yang, Q., et al. (2014) Application of BOX theory in Multi-Stage Emergency Resource Scheduling-Taking Emergency Response Stage as an Example. China Safety Science Journal, 24, 159-165.

[15]   Roger, C.S. and David, B.L. (1989) Creativity and Learning in a Case-Based Explainer. Artificial Intelligence, 40, 353-385.
http://dx.doi.org/10.1016/0004-3702(89)90053-2

 
 
Top