A Nonlinear Dynamic Logistics Model for Disaster Response under Uncertainty


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


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