Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-23T21:59:14.468Z Has data issue: false hasContentIssue false

A Stochastic Programming Model for Decision-Making Concerning Medical Supply Location and Allocation in Disaster Management

Published online by Cambridge University Press:  05 June 2017

Samad Barri Khojasteh*
Affiliation:
Sakarya University, Department of Industrial Engineering, Sakarya, Turkey
Irfan Macit
Affiliation:
Cukurova University, Department of Industrial Engineering, Adana, Turkey
*
Correspondence and reprint requests to Samad Barri Khojasteh, Sakarya University, Department of Industrial Engineering, 54187Serdivan, Sakarya, Turkey (e-mail: samad.khojasteh@gmail.com).

Abstract

We propose a stochastic programming model as a solution for optimizing the problem of locating and allocating medical supplies used in disaster management. To prepare for natural disasters, we developed a stochastic optimization approach to select the storage location of medical supplies and determine their inventory levels and to allocate each type of medical supply. Our model also captures disaster elaborations and possible effects of disasters by using a new classification for major earthquake scenarios. We present a case study for our model for the preparedness phase. As a case study, we applied our model to earthquake planning in Adana, Turkey. The experimental evaluations showed that the model is robust and effective. (Disaster Med Public Health Preparedness. 2017;11:747–755)

Type
Concepts in Disaster Medicine
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Shishebori, D, Jabalameli, M. Improving the efficiency of medical services systems: a new integrated mathematical modeling approach. Math Probl Eng. 2013;2013:13. http://www.hindawi.com/journals/mpe/2013/649397/abs/. Accessed August 9, 2014.Google Scholar
2. Rennemo, SJ, , KF, Hvattum, LM, et al. A three-stage stochastic facility routing model for disaster response planning. Transp Res Part E Logist Transp Rev. 2014;62:116-135. doi: 10.1016/j.tre.2013.12.006.CrossRefGoogle Scholar
3. Lu, C-CC. Robust weighted vertex p-center model considering uncertain data: An application to emergency management. Eur J Oper Res. 2013;230(1):113-121. doi: 10.1016/j.ejor.2013.03.028 Google Scholar
4. Mirzapour, S, Wong, K, Govindan, K. A capacitated location-allocation model for flood disaster service operations with border crossing passages and probabilistic demand locations. Math Probl Eng. 2013;2013:11. http://www.hindawi.com/journals/mpe/2013/507953/abs/. Accessed August 9, 2014.Google Scholar
5. Jia, H, Ordóñez, F, Dessouky, M. A modeling framework for facility location of medical services for large-scale emergencies. IIE Trans. 2007;39(1):41-55. doi: 10.1080/07408170500539113 Google Scholar
6. Mete, HHO, Zabinsky, ZZB. Stochastic optimization of medical supply location and distribution in disaster management. Int J Prod Econ. 2010;126(1):76-84. doi: 10.1016/j.ijpe.2009.10.004 Google Scholar
7. Barbarosoǧlu, G, Arda, Y. A two-stage stochastic programming framework for transportation planning in disaster response. J Oper Res Soc. 2004;55(1):43-53.CrossRefGoogle Scholar
8. Lamiri, M, Xie, X, Dolgui, A, et al. A stochastic model for operating room planning with elective and emergency demand for surgery. Eur J Oper Res. 2008;185(3):1026-1037. doi: 10.1016/j.ejor.2006.02.057 CrossRefGoogle Scholar
9. Beraldi, P, Bruni, ME, Conforti, D. Designing robust emergency medical service via stochastic programming. Eur J Oper Res. 2004;158(1):183-193. doi: 10.1016/S0377-2217(03)00351-5 Google Scholar
10. Wang, J, Yang, H, Zhu, J. A two-stage stochastic programming model for emergency resources storage region division. Syst Eng Procedia. 2012;5:125-130. doi: 10.1016/j.sepro.2012.04.020 Google Scholar
11. Lv, Y, Huang, GH, Guo, L, et al. A scenario-based modeling approach for emergency evacuation management and risk analysis under multiple uncertainties. J Hazard Mater. 2013;246–247:234-244. doi: 10.1016/j.jhazmat.2012.11.009 CrossRefGoogle ScholarPubMed
12. Salman, FS, Yücel, E. Emergency facility location under random network damage: insights from the Istanbul case. Comput Oper Res. 2015;62:266-281. doi: 10.1016/j.cor.2014.07.015 CrossRefGoogle Scholar
13. Edrissi, A, Poorzahedy, H, Nassiri, H, et al. A multi-agent optimization formulation of earthquake disaster prevention and management. Eur J Oper Res. 2013;229(1):261-275. doi: 10.1016/j.ejor.2013.03.008 Google Scholar
14. Liu, C, Fan, Y, Ordóñez, F. A two-stage stochastic programming model for transportation network protection. Comput Oper Res. 2009;36(5):1582-1590. doi: 10.1016/j.cor.2008.03.001 Google Scholar
15. Caunhye, AM, Nie, X, Pokharel, S. Optimization models in emergency logistics: a literature review. Socioecon Plann Sci. 2012;46(1):4-13. doi: 10.1016/j.seps.2011.04.004 Google Scholar
16. Sazvar, Z, Mirzapour Al-e-hashem, SMJ, Baboli, A, et al. A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products. Int J Prod Econ. 2014;150:140-154. doi: 10.1016/j.ijpe.2013.12.023 Google Scholar
17. Tricoire, F, Graf, A, Gutjahr, WJ. The bi-objective stochastic covering tour problem. Comput Oper Res. 2012;39(7):1582-1592. doi: 10.1016/j.cor.2011.09.009 Google Scholar
18. Alizadeh, SM, Marcotte, P, Savard, G. Two-stage stochastic bilevel programming over a transportation network. Transp Res Part B Methodol. 2013;58:92-105. doi: 10.1016/j.trb.2013.10.002 Google Scholar
19. Adana Kent Konseyi Publications. Plan of Disaster Action and Scenarios. Adana; 2008. https://www.afad.gov.tr/en/. Accessed April 17, 2017.Google Scholar