Mathematical Model of Location-Allocation in the Logistics of Relief Goods Response in Emergency Situations (Case Study of Tehran and Suburbs Urban Railway Operation Company)

Authors

https://doi.org/10.48314/ijorai.v1i1.52

Abstract

One of the important logistics strategies to improve performance and reduce relief time is to locate and establish aid distribution centers near vulnerable areas, the presence of these distribution centers in suitable locations can lead to a successful rescue operation, prioritizing according to the current guidelines. The goods needed for relief are from Red Crescent warehouses to relief centers, but after natural disasters, roads and relief routes have been damaged, for this purpose, in this research, the use of rail vehicles and subway infrastructure in Tehran metropolis has been proposed, which in addition to Reducing the response time to the requests of people in need can also solve the problem of blocked routes, and by using the policy of decentralization, we can see an increase in the quality of sending and a reduction in the problems caused by the concentration and accumulation of relief goods. The time and cost of aid delivery has been modeled, and by solving the mathematical model by games software and the input data of a case study, we will locate and select the best aid distribution stations among the candidate stations. 

Keywords:

Mathematical model, Localization, Allocation of relief goods, Response in emergency situations, Metro

References

  1. [1] Shariat Mahimani, A. (2005). Feasibility study of applying crisis management in road transportation in the country [Thesis]. https://civilica.com/doc/1047785/
  2. [2] Nobakht, M. (2013). A set of crisis management guidelines in the field of healthcare and treatment of unexpected events. Medical community mobilization organization. https://b2n.ir/bg6667
  3. [3] Najafi, M., Zanjirani Farahani, R., & Afrazeh, A. (2007). Location and inventory management of crisis management centers. The 5th international conference on industrial engineering. Civilica. https://civilica.com/doc/19366
  4. [4] Malali Shirtari, S. (2021). Comparison and functioning of safety, crisis management and risk management in the transportation sector. Scientific journal of modern research approaches in management and accounting, 5(19). 1567-1580. https://majournal.ir/index.php/ma/article/view/1210
  5. [5] Liu, Y., & Guo, B. (2014). A lexicographic approach to postdisaster relief logistics planning considering fill rates and costs under uncertainty. Mathematical problems in engineering, 2014(1), 939853.
  6. [6] https://doi.org/10.1155/2014/939853
  7. [7] Dufour, É., Laporte, G., Paquette, J., & Rancourt, M. (2018). Logistics service network design for humanitarian response in East Africa. Omega, 74, 1–14. https://doi.org/10.1016/j.omega.2017.01.002
  8. [8] Bilau, A. A., Witt, E., & Lill, I. (2017). Analysis of measures for managing issues in post-disaster housing reconstruction. Buildings, 7(2), 29. https://doi.org/10.3390/buildings7020029
  9. [9] Collins, M., Neville, K., Hynes, W., & Madden, M. (2016). Communication in a disaster-the development of a crisis communication tool within the S-HELP project. Journal of decision systems, 25(1), 160–170. https://doi.org/10.1080/12460125.2016.1187392
  10. [10] Arambepola, N. M. S. I., Rahman, M. A., & Tawhid, K. (2014). Planning needs assessment for responding to large disaster events in cities: Case study from dhaka, bangladesh. Procedia economics and finance, 18, 684–692. https://doi.org/10.1016/S2212-5671(14)00991-5
  11. [11] Görmez, N., M, K., & and Salman, F. S. (2011). Locating disaster response facilities in Istanbul. Journal of the operational research society, 62(7), 1239–1252. https://doi.org/10.1057/jors.2010.67
  12. [12] Tzeng, G. H., Cheng, H. J., & Huang, T. D. (2007). Multi-objective optimal planning for designing relief delivery systems. Transportation research part e: Logistics and transportation review, 43(6), 673–686. https://doi.org/10.1016/j.tre.2006.10.012
  13. [13] Boloori, A., & Zanjirani Farahani, R. (2012). Facility location dynamics: An overview of classifications and applications. Computers & industrial engineering, 62, 408–420. http://dx.doi.org/10.1016/j.cie.2011.09.018
  14. [14] Afshar, A., & Haghani, A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-economic planning sciences, 46(4), 327–338. https://doi.org/10.1016/j.seps.2011.12.003
  15. [15] Ishii, H., & Lee, Y. L. (2013). Mathematical ranking method for emergency facility location problem with block-wisely different accident occurrence probabilities. Procedia computer science, 22, 1065–1072. https://doi.org/10.1016/j.procs.2013.09.192
  16. [16] Zhang, J., Dong, M., & Frank Chen, F. (2013). A bottleneck Steiner tree based multi-objective location model and intelligent optimization of emergency logistics systems. Robotics and computer-integrated manufacturing, 29(3), 48–55. https://doi.org/10.1016/j.rcim.2012.04.012
  17. [17] Kaufman, Y. J., Tanré, D., Gordon, H. R., Nakajima, T., Lenoble, J., Frouin, R., Grassl, H., Herman, B. M., King, M. D., & Teillet, P. M. (1997). Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. Journal of geophysical research: Atmospheres, 102(14), 16815–16830. https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/97JD01496
  18. [18] Zhang, Y., Snyder, L. V, Qi, M., & Miao, L. (2016). A heterogeneous reliable location model with risk pooling under supply disruptions. Transportation research part b: Methodological, 83, 151–178. https://doi.org/10.1016/j.trb.2015.11.009
  19. [19] Zhang, W., & Liu, W. (2007). IFCM: Fuzzy clustering for rule extraction of interval type-2 fuzzy logic system. 2007 46th IEEE conference on decision and control (pp. 5318–5322). IEEE. https://doi.org/10.1109/CDC.2007.4434426
  20. [20] Nolz, P. C., Semet, F., & Doerner, K. F. (2011). Risk approaches for delivering disaster relief supplies. OR spectrum, 33, 543–569. https://doi.org/10.1007/s00291-011-0258-z
  21. [21] Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-economic planning sciences, 46(1), 23–32. https://doi.org/10.1016/j.seps.2011.05.002
  22. [22] Helbing, D., & Kühnert, C. (2003). Assessing interaction networks with applications to catastrophe dynamics and disaster management. Physica a: Statistical mechanics and its applications, 328(3), 584–606. https://doi.org/10.1016/S0378-4371(03)00519-3
  23. [23] Prizzia, R., & Helfand, G. (2001). Emergency preparedness and disaster management in Hawaii. Disaster prevention and management-disaster prev manag, 10, 173–182. http://dx.doi.org/10.1108/09653560110395313
  24. [24] Nateghi-E, F., & Izadkhah, Y. O. (2004). Earthquake disaster management planning in health care facilities. Disaster prevention and management, 13, 130–135. http://dx.doi.org/10.1108/09653560410534261
  25. [25] Nateghi-E, F. (2001). Earthquake scenario for the mega-city of Tehran. Disaster prevention and management, 10, 95–101. http://dx.doi.org/10.1108/09653560110388618

Downloads

Published

2025-03-12

Issue

Vol. 1 No. 1 (2025): International Journal of Operations Research and Artificial Intelligence (IJORAI)

Section

Articles

How to Cite

saeidi, M. . (2025). Mathematical Model of Location-Allocation in the Logistics of Relief Goods Response in Emergency Situations (Case Study of Tehran and Suburbs Urban Railway Operation Company). International Journal of Operations Research and Artificial Intelligence , 1(1), 11-19. https://doi.org/10.48314/ijorai.v1i1.52

Similar Articles

You may also start an advanced similarity search for this article.