Disaster logistics flexible planning model: A sample planning for Kütahya province

Year 2026, Issue: 064 , 29 - 47 , 30.03.2026

Hakan Gündüz , Tamer Eren

https://doi.org/10.59313/jsr-a.1738784

https://izlik.org/JA48ED43LS

Abstract

Disaster management covers all facilities focusing for reducing the effects of all mitigation operations. Generally, they are classified as pre disaster and post-disaster facilities. Primarily it contains the planning operations of pre disaster facilities sustaining the effective intervention operations. The disaster logistics is an inseparable part of disaster management facilities. The main part of the disaster management is delivering of the sufficient sources to the pre described places. This depends on the careful planning, application and updating. In this respect, Kütahya province analyzed with respect to disaster logistics perspective. A model was developed by means of Mixed Integer Programming (MIP) method for pre disaster and post disaster processes. This method helps for solving hard problems containing integer and continuous variables. This model generates an initial logistic plan with respect to possible effects of the disasters. In addition, because of its flexible structure, it can produce updated solutions with respect to information obtained after disaster events. Our model can serve to decision makers as a convenient tool for disaster management facilities.

Keywords

Disaster management , Disaster logistics , Humanitarian logistics , Operations research

References

• [1] G. Kovacs and K. Spens, “Identifying challenges in humanitarian logistics,” Int J Phys Distrib Logist Manag., vol. 39, no. 6, pp. 506–28, 2009.
• [2] K. Govindan, H. Mina, B. Alavi, “A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19),” Transp Res Part E Logist Transp Rev., vol. 138, no. 101967, 2020, doi:10.1016/j.tre.2020.101967.
• [3] A.A. Javid, P. Seyedi, S.A. Syam, “A survey of healthcare facility location,” Comput Oper Res., vol. 79, pp. 223–63, 2017, doi:10.1016/j.cor.2016.05.018.
• [4] S. Tofighi, S.A, S. Mansouri, ‘Humanitarian logistics network design under mixed uncertainty,’ Eur J Oper Res., vol. 250, no. , pp. 239–50, 2016, doi:10.1016/j.ejor.2015.08.059.
• [5] TM. Choi, “Innovative "bring-service-near-your-home" operations under coronavirus (COVID-19/SARS-CoV-2) outbreak: can logistics become the messiah?,” Transp Res Part E Logist Transp Rev., vol. 140, no. 101961, 2020, doi:10.1016/j.tre.2020.101961.
• [6] L. Özdamar, MA Ertem, “Models, solutions and enabling technologies in humanitarian logistics,” Eur J Oper Res., vol. 244, no. 1, pp. 55–65, 2015, doi:10.1016/j.ejor.2014.11.030.
• [7] F. Kılcı, BY. Kara, B. Bozkaya, “Locating temporary shelter areas after an earthquake: A case for Turkey,” Eur J Oper Res., vol. 243, no. 1, pp. 323–32, 2015, doi:10.1016/j.ejor.2014.11.035.
• [8] O. Rodríguez-Espíndola, S. Chowdhury, A. Beltagui, P. Albores, “The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, artificial intelligence and 3D printing,” Int J Prod Res., vol. 58, no. 15, pp. 4610–30, 2020, doi:10.1080/00207543.2020.1761565.
• [9] S. Gupta, MK. Starr, RZ. Farahani, N. Matinrad, “Disaster management from a POM perspective: mapping a new domain,” Prod Oper Manag., vol. 25, no. 10, pp. 1611–37, 2016, doi:10.1111/poms.12591.
• [10] D. Alem, A. Clark, A. Moreno, “Stochastic network models for logistics planning in disaster relief,” Eur J Oper Res., vol. 255, no. 1, pp. 187–206, 2016, doi:10.1016/j.ejor.2016.04.04.
• [11] Chowdhury S, Emelogu A, Marufuzzaman M, Nurre SG, Bian LK. “Drones for disaster response and relief operations: A continuous approximation model” Int J Prod Econ., vol. 188, pp. 167–84, 2017, doi:10.1016/j.ijpe.2017.03.024.
• [12] Zhou YW, Liu J, Zhang YT, Gan XH., “A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems”, Transp Res E Logist Transp Rev., vol. 99, pp. 77–95, 2017, doi:10.1016/j.tre.2016.12.011.
• [13] Ahmadi M, Seifi A, Tootooni B., “A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district”, Transp Res E Logist Transp Rev., vol. 75, pp. 145–63, 2015, doi:10.1016/j.tre.2015.01.008.
• [14] Diabat A, Jabbarzadeh A, Khosrojerdi A., “A perishable product supply chain network design problem with reliability and disruption considerations”, Int J Prod Econ., vol. 212, pp. 125–38, 2019, doi:10.1016/j.ijpe.2018.09.018.
• [15] Sinha A, Kumar P, Rana NP, Islam R, Dwivedi YK., “Impact of internet of things (IoT) in disaster management: a task-technology fit perspective”, Ann Oper Res., vol. 283, no. (1–2), pp. 759–94, 2019, doi:10.1007/s10479-017-2658-1.
• [16] Cao CJ, Liu Y, Tang O, Gao XH., “A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains”, Int J Prod Econ., vol. 235, no. 108081, 2021, doi:10.1016/j.ijpe.2021.108081.
• [17] Dubey R, Altay N, Blome C., “Swift trust and commitment: The missing links for humanitarian supply chain coordination?”, Ann Oper Res., vol. 283, no. (1–2), pp. 159–77, 2019, doi:10.1007/s10479-017-2676-z.
• [18] Abazari SR, Aghsami A, Rabbani M., “Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters”, Socio Econ Plan Sci., vol. 74, no. 100933, 2021, doi:10.1016/j.seps.2020.100933.
• [19] Torabi SA, Shokr I, Tofighi S, Heydari J., “Integrated relief pre-positioning and procurement planning in humanitarian supply chains”, Transp Res E Logist Transp Rev., vol. 113, pp. 123–46, 2018, doi:10.1016/j.tre.2018.03.012.
• [20] Rodríguez-Espíndola O, Albores P, Brewster C., “Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods”, Eur J Oper Res., vol. 264, no. 3, pp. 978–93, 2018, doi:10.1016/j.ejor.2017.01.021.
• [21] Huang K, Jiang YP, Yuan YF, Zhao LD., “Modeling multiple humanitarian objectives in emergency response to large-scale disasters”, Transp Res E Logist Transp Rev., vol. 75, pp. 1–17, 2015, doi:10.1016/j.tre.2014.11.007.
• [22] Sabouhi F, Bozorgi-Amiri A, Moshref-Javadi M, Heydari M., “An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study”, Ann Oper Res., vol. 283, no. (1–2), pp. 643–77, 2019, doi:10.1007/s10479-018-2807-1.
• [23] Manopiniwes W, Irohara T., “Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response”, Int J Prod Res., vol. 55, no. 4. pp. 979–96, 2017, doi:10.1080/00207543.2016.1211340.
• [24] Tavana M, Abtahi AR, Di Caprio D, Hashemi R, Yousefi-Zenouz R., “An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations”, Socio Econ Plan Sci., vol. 64, pp. 21–37, 2018, doi:10.1016/j.seps.2017.12.004.
• [25] Elçi Ö, Noyan N., “A chance-constrained two-stage stochastic programming model for humanitarian relief network design”, Transp Res B Methodol., vol. 108, pp. 55–83, 2018, doi:10.1016/j.trb.2017.12.002.
• [26] Hu SL, Dong ZS. Supplier selection and pre-positioning strategy in humanitarian relief. Omega (Westport)., vol. 83, pp. 287–98, 2019, doi:10.1016/j.omega.2018.10.011.
• [27] Ghasemi P, Khalili-Damghani K, Hafezalkotob A, Raissi S., “Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)”, Socio Econ Plan Sci., vol. 71, no. 100745, 2020, doi:10.1016/j.seps.2019.100745.
• [28] Caunhye AM, Zhang YD, Li MZ, Nie XF., “A location-routing model for prepositioning and distributing emergency supplies”, Transp Res E Logist Transp Rev., vol. 90, pp. 161–76, 2016, doi:10.1016/j.tre.2015.10.011.
• [29] Nezhadroshan AM, Fathollahi-Fard AM, Hajiaghaei-Keshteli M., “A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities”, Int J Syst Sci Oper Logist., vol. 8, no. 4, pp. 321–47, 2021, doi:10.1080/23302674.2020.1769766.
• [30] He YX, Liu N., “Methodology of emergency medical logistics for public health emergencies”, Transp Res E Logist Transp Rev. vol. 79, pp. 178–200, 2015, doi:10.1016/j.tre.2015.04.007.
• [31] Venkatesh VG, Zhang A, Deakins E, Luthra S, Mangla S., “A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains”, Ann Oper Res., vol. 283, no. (1–2), pp. 1517–50, 2019, doi:10.1007/s10479-018-2981-1.
• [32] AFAD Kütahya İl Afet Risk Azaltma Planı (İrap) sh.96, 2021.
• [33]Karayolları Genel Müdürlüğü internet sayfası, [Online]. Available: https://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Uzakliklar/ilcedenIlceyeMesafe.aspx
• [34] İstanbul Kalkınma Ajansı, İstanbul İli Afet Lojistik Plani Kilavuzu, İstanbul, sh. 176-179, 31/01/2013.