Abstract
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.
Highlights
In the disaster management process, the implementation decisions concerned with the mitigation, preparedness, response, and recovery stages must be made to alleviate these events’ negative effects, and to keep the situation under control
While pre-disaster management considers the period before the disaster occurs, the disaster situation is the aftermath of the event, and post-disaster is the period between the occurrence of the disaster and the return to the normal conditions [2]
We aim to minimize the number of unserved casualties, the number of ambulances as well as the total transportation time by creating scenarios based on uncertain factors
Summary
In the disaster management process, the implementation decisions concerned with the mitigation, preparedness, response, and recovery stages must be made to alleviate these events’ negative effects, and to keep the situation under control. Disaster management operations include preparation, supply, transportation, location, distribution, tracking, and storage problems. These operations play an important role in preventing damages [1]. The three phases of disaster management are pre-disaster, disaster situation (disaster) and post-disaster. Some studies divide the process into two phases as pre-disaster and post-disaster. According to Caunhye et al [3], the main decisions are evaluated in pre-disaster operations (facility location, pre-positioning and evacuation) and post-disaster operations (involving relief distribution and casualty transportation). Pre-disaster and post-disaster stages were evaluated together to plan the casualty transportation process from triage points to the hospitals
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