A Nonlinear Dynamic Logistics Model for Disaster Response under Uncertainty
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Authors
M. Khorsi
- 1Department of Industrial Engineering, Tafresh University, Tafresh, Iran.
A. Bozorgi-amiri
- 2 Department of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran.
B. Ashjari
- 1Department of Industrial Engineering, Tafresh University, Tafresh, Iran.
Abstract
To save lives and alleviate suffering, the response to emergency must be timely, effective, appropriate, and well organized. Logistics can play a key role. This paper presents a multi-objective dynamic stochastic model for a complex logistical problem in disaster relief operations. Prior to the disaster onset, design decisions including the number and location of local distribution centers needed as well as their inventory levels for each type of emergency supply are made. After the disaster onset, the designed network will use to conduct daily operational decisions over a planning horizon that covers the disaster duration. The first objective function attempts to minimize the sum of the expected value of the total cost of the relief chain; at the same time the model aims to maximize the affected areas’ satisfaction levels through minimizing the sum of the maximum shortages in the affected areas. A case study is presented to illustrate the potential applicability of our model for disaster planning for earthquake scenarios in the megacity of Tehran. The results demonstrate the practicability of the proposed multi-objective stochastic model.
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ISRP Style
M. Khorsi, A. Bozorgi-amiri, B. Ashjari, A Nonlinear Dynamic Logistics Model for Disaster Response under Uncertainty, Journal of Mathematics and Computer Science, 7 (2013), no. 1, 63 - 72
AMA Style
Khorsi M., Bozorgi-amiri A., Ashjari B., A Nonlinear Dynamic Logistics Model for Disaster Response under Uncertainty. J Math Comput SCI-JM. (2013); 7(1):63 - 72
Chicago/Turabian Style
Khorsi, M., Bozorgi-amiri, A., Ashjari, B.. "A Nonlinear Dynamic Logistics Model for Disaster Response under Uncertainty." Journal of Mathematics and Computer Science, 7, no. 1 (2013): 63 - 72
Keywords
- Disaster relief logistics
- stochastic programming
- Multi-objective optimization.
MSC
References
-
[1]
, , http://www.emdat.com. , ()
-
[2]
A. Afshar, A. Haghani, Modeling integrated supply chain logistics in real-time large-scale disaster relief operations, Socio-Economic Planning Sciences, Article in press, 46 (2012), 1-12
-
[3]
Y. H. Lin, R. Batta, P. Rogerson, A. Blatt, M. Flanigan, A logistics model for emergency supply of critical items in the aftermath of a disaster, Socio-Economic Planning Sciences, Vol. 45, No. 4 (2011), 132-145
-
[4]
G. H. Tzeng, HJ. Cheng, TD. Huang, Multi-objective optimal planning for designing relief delivery systems, Transportation Research Part E, Vol.43, No. 6 (2007), 673–686
-
[5]
B. Balcik, B. M. Beamon, Facility location in humanitarian relief, Journal of Logistics: Research and Applications, Vol. 11, No. 2 (2008), 101–121
-
[6]
G. Barbarosoglu, Y. Arda, A two-stage stochastic programming framework for transportation planning in disaster response, Journal of the Operational Research Society, 55 (2004), 43–53
-
[7]
A. Bozorgi-Amiri, S. Jabalameli, S. M. J. Mirzapour, A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty, OR Spectrum, 35 (2012), 905–933
-
[8]
M. S. Chang, Y. L. Tseng, J. W .Chen, A scenario planning approach for the flood emergency logistics preparation problem under uncertainty, Transportation Research Part E, Vol. 43, No. 6 (2007), 737-754
-
[9]
H. Jia, F. Ordonez, M. Dessouky, A modeling framework for facility location of medical services for large-scale emergencies, IIE Transactions, Vol. 39, No. 1 (2007), 41–55
-
[10]
J. Salmeron, A. Apte, Stochastic optimization for natural disaster asset prepositioning, Production and Operations Management, Vol. 19, No. 5 (2010), 561–574
-
[11]
O. N. Mete, Z. Zabinsky, Stochastic optimization of medical supply distribution, International Journal of Production Economics, 126 (2010), 76–84
-
[12]
C. G. Rawls, M. A. Turnsquist, Pre-positioning of emergency supplies for disaster response, Transportation Research Part B, Vol. 44, No. 4 (2010), 521–534
-
[13]
G. Mavrotas, Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems, Application Math Computer, Vol. 213, No. 2 (2009), 455–465
-
[14]
M. Ashtari, D. Hatzfeld, N. Kamalian, Microseismicity in the region of Tehran, Tectonophysics, Vol. 395, No. 3 (2005), 193-208