Abstract
The increasing frequency of natural disasters and the necessity of proper planning to minimize the impact and casualties of such crises have always been matters of great concern to human societies. In this study, a hybrid mathematical-simulative location-allocation model is proposed to carry out disaster management (DM) efforts with maximum coverage in the immediate aftermath of an earthquake. The proposed model consists of two phases: determining the optimal location of the temporary emergency stations (TECs), followed by optimal and hierarchical allocation of casualties to said temporary medical centers (TMCs). Given the contradictory nature of the model’s two objectives, that is, minimizing the cost of setting up TMCs and the time taken to transfer casualties to TMC. In the second phase, a simulation-based optimization approach is employed to simulate casualties’ behavior at the onset of the disaster and to determine the optimal capacity of the medical centers. The findings indicate that the costs and distance traveled by casualties during the earthquake have been reduced by 15%.
Original language | English |
---|---|
Pages (from-to) | 407-432 |
Number of pages | 26 |
Journal | Simulation |
Volume | 98 |
Issue number | 5 |
Early online date | 27 Dec 2021 |
DOIs | |
Publication status | Published - May 2022 |
Externally published | Yes |
Bibliographical note
Funding Information:The authors thank the Tehran Disaster Mitigation and Management Organization (TDMMO) for their valuable data, insights, and suggestions. Their comments certainly helped enhance the accuracy and significance of our paper. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© The Author(s) 2021.
Keywords
- disaster management
- hierarchical mathematical model
- Simulation-based optimization
- temporary medical centers
- ε-constraint method
ASJC Scopus subject areas
- Software
- Modelling and Simulation
- Computer Graphics and Computer-Aided Design