A two-stage stochastic model for location planning of temporary medical centers for disaster response

Öksüz M. K., Satoğlu Ş. I.

International Journal of Disaster Risk Reduction, cilt.44, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.ijdrr.2019.101426
  • Dergi Adı: International Journal of Disaster Risk Reduction
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Humanitarian logistics, Disaster management, Emergency medical center, Field hospital, Facility location, Stochastic programming, Mass casualty events, FACILITY LOCATION, OPTIMIZATION MODEL, NETWORK DESIGN, OR/MS RESEARCH, EMERGENCY, EARTHQUAKE, DECISIONS, SUPPLIES, PREPAREDNESS, INJURIES
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2019 Elsevier LtdDevastating effects of disasters and global crises on people increases the importance of humanitarian logistics studies for pre and post-disaster stages. Location planning of Temporary Medical Centers/field hospitals is one of the most important problems for disaster response. We aimed to determine the location and number of temporary medical centers in case of disasters by considering the locations of the existing hospitals, casualty classification (triage), capacities of medical centers and possibilities of damage to the roads and hospitals. Besides, we aimed to assign different casualty classes to these medical centers for emergency medical response by considering the distances between disaster areas and medical centers. For this purpose, a two-stage stochastic programming model was developed. The proposed model finds an optimal TMC location solution while minimizing the total setup cost of the TMCs and the expected total transportation cost by considering casualty types, demand, possibilities of damage to the roads and hospitals, and distance between the disaster areas and the medical cente2rs. In the model, α-reliability constraints for the expected number of unassigned casualties were also used. Besides, the model was reformulated without triage, in order to understand the impact of casualty classification on the solution of the problem. We performed a real case study for the district of Kartal expected to be widely damaged in the possible Istanbul earthquake, and a sensitivity analysis was made. The analysis of the results offer some managerial insights associated with the number of temporary medical centers’ needed, their locations, and additional hospital capacity requirements.