A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

OR Spectrum ◽  
2011 ◽  
Vol 35 (4) ◽  
pp. 905-933 ◽  
Author(s):  
Ali Bozorgi-Amiri ◽  
M. S. Jabalameli ◽  
S. M. J. Mirzapour Al-e-Hashem
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Disaster Relief ◽  
Multi Objective ◽  
Stochastic Programming Model ◽  
Robust Stochastic Programming

scholarly journals Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 316
Author(s):  
Nadide Caglayan ◽  
Sule Itir Satoglu
Keyword(s):  
Decision Support ◽  
Stochastic Programming ◽  
Programming Model ◽  
Decision Support Tool ◽  
Data Driven ◽  
Two Stage ◽  
Support Tool ◽  
Transportation Time ◽  
Multi Objective ◽  
Stochastic Programming Model

Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points.

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