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Journals / International Journal of Engineering and Geosciences / 2026 / Volume: 10 Issue: 2

Multi-dimensional optimization of nuclear emergency shelters: balancing capacity, evacuation efficiency, and supply accessibility

Maryna Batur(İSTANBUL TEKNİK ÜNİVERSİTESİ)0000-0001-9284-8858
Reha Metin Alkan(İSTANBUL TEKNİK ÜNİVERSİTESİ)0000-0002-1981-9783
Himmet Karaman(İSTANBUL TEKNİK ÜNİVERSİTESİ)0000-0003-4923-3561
Haluk Özener(BOĞAZİÇİ ÜNİVERSİTESİ)0000-0003-2531-3030
Pages
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Publication Date
2025-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, the research supports more balanced shelter allocations in nuclear emergencies, improving both immediate and long-term disaster response strategies for affected populations

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Effective shelter location-allocation is critical in nuclear emergencies to ensure rapid, safe evacuation and resource access for affected populations.
📊
Key Findings & Data:This study presents a multi-dimensional optimization model for shelter allocation within humanitarian logistics, balancing evacuation time, supply accessibility, and shelter capacity.
💡
Conclusion & Impact:Overall, the research supports more balanced shelter allocations in nuclear emergencies, improving both immediate and long-term disaster response strategies for affected populations
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:nuclear emergency · road network analysis · shelter optimization

Abstract

Effective shelter location-allocation is critical in nuclear emergencies to ensure rapid, safe evacuation and resource access for affected populations. This study presents a multi-dimensional optimization model for shelter allocation within humanitarian logistics, balancing evacuation time, supply accessibility, and shelter capacity. Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA), the model optimizes trade-offs among competing objectives. The first objective minimizes evacuation time, the second ensures adequate supply access, and the third prevents shelter overcrowding. Validated through k-fold cross-validation, the model reveals spatial biases: evacuees often cluster in nearby shelters, leading to overcrowding in dense areas and underuse in others. This analysis suggests adding flexible shelters in high-density zones to enhance response efficiency. Overall, the research supports more balanced shelter allocations in nuclear emergencies, improving both immediate and long-term disaster response strategies for affected populations

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Cite This Article

Batur, M., Alkan, R. M., Karaman, H., Özener, H. (2025). Multi-dimensional optimization of nuclear emergency shelters: balancing capacity, evacuation efficiency, and supply accessibility. International Journal of Engineering and Geosciences, Volume: 10 Issue: 2. https://doi.org/10.26833/ijeg.1596244

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References (60)

  1. Ohba, T., Tanigawa, K., Liutsko, L. (2021). Evacuation after a nuclear accident: Critical reviews of past nuclear accidents and proposal for future planning. Environment international, (148), 106379. https://doi.org/10.1016/j.envint.2021.106379
  2. Batur, M., Alkan, R. M. (2023). What can we learn from past nuclear accidents? A comparative assessment of emergency response to accidents at the Three Mile Island, Chernobyl, and Fukushima Nuclear Power Plants. Advanced Land Management, 3(2), 76-89. https://publish.mersin.edu.tr/index.php/alm/article/view/1075
  3. Hasegawa, A., Ohira, T., Maeda, M., Yasumura, S., Tanigawa, K. (2016). Emergency responses and health consequences after the Fukushima accident; evacuation and relocation. Clinical Oncology, 28(4), 237-244. https://doi.org/10.1016/j.clon.2016.01.002
  4. Oka, Y. (2022). Risks and benefits of evacuation in TEPCO's Fukushima Daiichi nuclear power station accident. Progress in Nuclear Energy, 148, 104222. https://doi.org/10.1016/j.pnucene.2022.104222
  5. Sarı, S. & Türk, T. (2021). An investigation of urban development with geographical information systems: 100-year change of Sivas City, Turkey. International Journal of Engineering and Geosciences, 6(1), 51-63.
  6. Sohrabi, M., Ghasemi, M., Amrollahi, R., Khamooshi, C., Parsouzi, Z. (2013). Assessment of environmental public exposure from a hypothetical nuclear accident for Unit-1 Bushehr nuclear power plant. Radiation and environmental biophysics, 52, 235-244. https://doi.org/10.1007/s00411-013-0456-y
  7. Zhu, Y., Guo, J., Nie, C., Zhou, Y. (2014). Simulation and dose analysis of a hypothetical accident in Sanmen nuclear power plant. Annals of Nuclear Energy, 65, 207-213. https://doi.org/10.1016/j.anucene.2013.11.016
  8. Ramana, M. V., Nayyar, A. H., Schoeppner, M. (2016). Nuclear High-level Waste Tank Explosions: Potential Causes and Impacts of a Hypothetical Accident at India's Kalpakkam Reprocessing Plant. Science & Global Security, 24(3), 174-203. https://doi.org/10.1080/08929882.2016.1237661
  9. Batur, M., Alkan, R. M., Karaman, H., Ozener, H. (2024). Recommendations for Protective Actions Based on Projected Public Health Risks Following a Postulated Nuclear Power Plant Accident. Malaysian Journal of Fundamental and Applied Sciences, 20(4), 923-938. https://doi.org/10.11113/mjfas.v20n4.3561
  10. Mohammed Saeed, I. M., Saleh, M. A. M., Hashim, S., Hama, Y. M. S., Hamza, K., Al-Shatri, S. H. (2020). The radiological assessment, hazard evaluation, and spatial distribution for a hypothetical nuclear power plant accident at Baiji potential site. Environmental Sciences Europe, 32, 1-12. https://doi.org/10.1186/s12302-020-0288-8
  11. Murray-Tuite, P., Wolshon, B. (2013). Evacuation transportation modeling: An overview of research, development, and practice. Transportation Research Part C: Emerging Technologies, 27, 25-45. https://doi.org/10.1016/j.trc.2012.11.005
  12. Park, S., Sohn, S., Jae, M. (2021). Cohort-based evacuation time estimation using TSIS-CORSIM. Nuclear Engineering and Technology, 53(6), 1979-1990. https://doi.org/10.1016/j.net.2020.11.028
  13. Herrera, N., Smith, T., Parr, S. A., Wolshon, B. (2019). Effect of trip generation time on evacuation time estimates. Transportation research record, 2673(11), 101-113. https://doi.org/10.1177/0361198119850793
  14. Hsu, Y. T., Peeta, S. (2015). Clearance time estimation for incorporating evacuation risk in routing strategies for evacuation operations. Networks and Spatial Economics, 15, 743-764. https://doi.org/10.1007/s11067-013-9195-5
  15. Parr, S. A., Herrera, N., Wolshon, B., Smith, T. (2020). Effect of manual traffic control on evacuation time estimates. Transportation research record, 2674(9), 809-819. https://doi.org/10.1177/0361198120932165
  16. Howard, E. E., Pasquini, L., Arbib, C., Di Marco, A., Clementini, E. (2021). Definition of an Enriched GIS Network for Evacuation Planning. In GISTAM, 241-252. https://doi.org/10.5220/0010452302410252
  17. Manfré, L. A., Cruz, B. B., Quintanilha, J. A. (2020). Urban settlements and road network analysis on the surrounding area of the Almirante Alvaro Alberto Nuclear Complex, Angra dos Reis, Brazil. Applied spatial analysis and policy, 13, 209-221. https://doi.org/10.1007/s12061-019-09299-2
  18. Hasnat, M. M., Islam, M. R., Hadiuzzaman, M. (2018). Emergency response during disastrous situation in densely populated urban areas: a gis based approach. Geographia Technica, 13(2), https://doi.org/10.21163/GT_2018.132.06
  19. Maqbool, A., Usmani, Z., Afzal, F., Razia, A. (2020). Disaster mitigation in Urban Pakistan using agent based modeling with GIS. ISPRS International Journal of Geo-Information, 9(4), 203. https://doi.org/10.3390/ijgi9040203
  20. Hwang, Y., Heo, G. (2021). Development of a radiological emergency evacuation model using agent-based modeling. Nuclear Engineering and Technology, 53(7), 2195-2206. https://doi.org/10.1016/j.net.2021.01.007
  21. Zhang, H., Zhao, Q., Cheng, Z., Liu, L., Su, Y. (2021). Dynamic path optimization with real‐time information for emergency evacuation. Mathematical Problems in Engineering, 2021(1), 3017607. https://doi.org/10.1155/2021/3017607
  22. Zeng, M. H., Wang, M., Chen, Y., Yang, Z. (2021). Dynamic evacuation optimization model based on conflict-eliminating cell transmission and split delivery vehicle routing. Safety science, 137, 105166. https://doi.org/10.1016/j.ssci.2021.105166
  23. Turcanu, C., Sala, R., Perko, T., Abelshausen, B., Oltra, C., Tomkiv, Y., Zeleznik, N. (2021). How would citizens react to official advice in a nuclear emergency? Insights from research in three European countries. Journal of Contingencies and Crisis Management, 29(2), 143-169. https://doi.org/10.1111/1468-5973.12327
  24. Fei, L., Chunhua, C., Fang, R., Yuan, C., Jingxian, Z., Jianye, W. (2023). Influence of human factors on emergency evacuation efficiency of nuclear power plant accident. Journal of Radiation Research and Radiation Processing, 41(4), 40602. 10.11889/j.1000-3436.2022-0128
  25. Ma, Y., Xu, W., Qin, L., Zhao, X. (2019). Site selection models in natural disaster shelters: A review. Sustainability, 11(2), 399. https://doi.org/10.3390/su11020399
  26. Şentürk, E. & Erener, A. (2017). Determination of temporary shelter areas in natural disasters by gis: A case study, Gölcük/Turkey. International Journal of Engineering and Geosciences, 2(3), 84-90.
  27. Wigati, S. S., Sopha, B. M., Asih, A. M. S., Sutanta, H. (2023). Geographic information system based suitable temporary shelter location for Mount Merapi eruption. Sustainability, 15(3), 2073. https://doi.org/10.3390/su15032073
  28. Bera, S., Gnyawali, K., Dahal, K., Melo, R., Li-Juan, M., Guru, B., Ramana, G. V. (2023). Assessment of shelter location-allocation for multi-hazard emergency evacuation. International Journal of Disaster Risk Reduction, 84, 103435. https://doi.org/10.1016/j.ijdrr.2022.103435
  29. Ozkan, B., Mete, S., Çelik, E., Özceylan, E. (2019). GIS-based maximum covering location model in times of disasters: the case of tunceli. Beykoz Akademi Dergisi, 100-111. https://doi.org/10.14514/BYK.m.26515393.2019.sp/100-111
  30. Sotelo-Salas, C., Monardes-Concha, C. A., Perez-Galarce, F., Santa Gonzales, R. (2024). A multi-objective optimization model for planning emergency shelters after a tsunami. Socio-Economic Planning Sciences, 93, 101909. https://doi.org/10.1016/j.seps.2024.101909
  31. Zhang, P., Zhang, H., Guo, D. (2015, November). Evacuation shelter and route selection based on multi-objective optimization approach. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, 1-5. https://doi.org/10.1145/2835596.2835598
  32. Hallak, J., Koyuncu, M., Miç, P. (2019). Determining shelter locations in conflict areas by multiobjective modeling: A case study in northern Syria. International journal of disaster risk reduction, 38, 101202. https://doi.org/10.1016/j.ijdrr.2019.101202
  33. Perez-Galarce, F., Canales, L. J., Vergara, C., Candia-Vejar, A. (2017). An optimization model for the location of disaster refuges. Socio-Economic Planning Sciences, 59, 56-66. https://doi.org/10.1016/j.seps.2016.12.001
  34. Kınay, O. B., Kara, B. Y., Saldanha-da-Gama, F., Correia, I. (2018). Modeling the shelter site location problem using chance constraints: A case study for Istanbul. European Journal of Operational Research, 270(1), 132-145. https://doi.org/10.1016/j.ejor.2018.03.006
  35. Eriskin, L., Karatas, M. (2022). Applying robust optimization to the shelter location–allocation problem: a case study for Istanbul. Annals of Operations Research, 339, 1589-1635. https://doi.org/10.1007/s10479-022-04627-1
  36. Gu, J., Zhou, Y., Das, A., Moon, I., Lee, G. M. (2018). Medical relief shelter location problem with patient severity under a limited relief budget. Computers & Industrial Engineering, 125, 720-728. https://doi.org/10.1016/j.cie.2018.03.027
  37. Zhu, J., Li, W., Li, H., Wu, Q., Zhang, L. (2017). A novel swarm intelligence algorithm for the evacuation routing optimization problem. Computational complexity, 1(1), 2.
  38. Salam, M. A., Khan, S. A. (2020). Lessons from the humanitarian disaster logistics management: A case study of the earthquake in Haiti. Benchmarking: An International Journal, 27(4), 1455-1473. https://doi.org/10.1108/BIJ-04-2019-0165
  39. Caunhye, A. M., Nie, X., Pokharel, S. (2012). Optimization models in emergency logistics: a literature review. Socio-Economic Planning Sciences, 46(1), 4-13. https://doi.org/10.1016/j.seps.2011.04.004
  40. Boonmee, C., Arimura, M., Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International journal of disaster risk reduction, 24, 485-498. https://doi.org/10.1016/j.ijdrr.2017.01.017
  41. Yang, W., Caunhye, A. M., Zhuo, M., Wang, Q. (2024). Integrated planning of emergency supply pre-positioning and victim evacuation. Socio-Economic Planning Sciences, 95, 101965. https://doi.org/10.1016/j.seps.2024.101965
  42. Ozbay, E., Çavuş, Ö., Kara, B. Y. (2019). Shelter site location under multi-hazard scenarios. Computers & operations research, 106, 102-118. https://doi.org/10.1016/j.cor.2019.02.008
  43. Praneetpholkrang, P., Huynh, V. N. (2020). Shelter site selection and allocation model for efficient response to humanitarian relief logistics. In Dynamics in Logistics: Proceedings of the 7th International Conference LDIC 2020, Bremen, Germany, 309-318. Springer International Publishing. https://doi.org/10.1007/978-3-030-44783-0_30
  44. Kanoun, I., Chabchoub, H., Aouni, B. (2010). Goal programming model for fire and emergency service facilities site selection. INFOR: Information Systems and Operational Research, 48(3), 143-153. https://doi.org/10.3138/infor.48.3.143
  45. Gormez, N., Koksalan, M., 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
  46. Xu, W., Zhao, X., Ma, Y., Li, Y., Qin, L., Wang, Y., Du, J. (2018). A multi-objective optimization based method for evaluating earthquake shelter location–allocation. Geomatics, Natural Hazards and Risk, 9(1), 662-677. https://doi.org/10.1080/19475705.2018.1470114
  47. Barzinpour, F., Esmaeili, V. (2014). A multi-objective relief chain location distribution model for urban disaster management. The International Journal of Advanced Manufacturing Technology, 70, 1291-1302. https://doi.org/10.1007/s00170-013-5379-x
  48. Burkart, C., Nolz, P. C., Gutjahr, W. J. (2017). Modelling beneficiaries’ choice in disaster relief logistics. Annals of Operations Research, 256, 41-61. https://doi.org/10.1007/s10479-015-2097-9
  49. Marler, R. T., Arora, J. S. (2010). The weighted sum method for multi-objective optimization: new insights. Structural and multidisciplinary optimization, 41, 853-862. https://doi.org/10.1007/s00158-009-0460-7
  50. Mamei, M., Bicocchi, N., Lippi, M., Mariani, S., Zambonelli, F. (2019). Evaluating origin–destination matrices obtained from CDR data. Sensors, 19(20), 4470. https://doi.org/10.3390/s19204470
  51. Urbanik, II. T. (2000). Evacuation time estimates for nuclear power plants. Journal of Hazardous Materials, 75(2-3), 165-180. https://doi.org/10.1016/S0304-3894(00)00178-3
  52. Zhang, X., Liu, C. A. (2023). Model averaging prediction by K-fold cross-validation. Journal of Econometrics, 235(1), 280-301. https://doi.org/10.1016/j.jeconom.2022.04.007
  53. Kyriakides, K. A. (2023). The Akkuyu Nuclear Power Plant in Turkey: Some Causes for Concern. Journal of Balkan and Near Eastern Studies, 25(3), 340-377. https://doi.org/10.1080/19448953.2022.2143855
  54. Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69
  55. Güven, O., Yıldırım, Ümit., Güler, C., & Kurt, M. A. (2024). Land use and land cover classes affected by the possible sea level rise in Mersin city center (Türkiye). Advanced GIS, 4(1), 15–23. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/1348
  56. Topaloglu, R. H. (2022). Investigation of Land Use/Land Cover change in Mersin using geographical object-based image analysis (GEOBIA). Advanced Remote Sensing, 2(2), 40–46. Retrieved from https://publish.mersin.edu.tr/index.php/arsej/article/view/247 Yilmaz, C. B., Sevimli, M. F., Demir, F., Yakar, M. (2021). Trend analysis of temperature and precipitation in Mediterranean region. Advanced GIS, 1(1), 15-21. Retrieved from https://publish.merisn.edu.tr/index.php/agis/article/view/60
  57. Balcı, D. (2022). Researching the use of infrastructure in land management. Advanced GIS, 2(1), 18–23. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/250
  58. General Directorate of Highways. The Republic of Turkey General Directorate of Highways. Republic of Turkey, www.kgm.gov.tr. Accessed on September 2024.
  59. Bijaber, N., Rochdi, A., Yessef, M., El Yacoubi, H. (2024). Mapping the structural vulnerability to drought in Morocco. International Journal of Engineering and Geosciences, 9(2), 264-280. https://doi.org/10.26833/ijeg.1404507
  60. Güven, O., Yıldırım, Ümit., Güler, C., & Kurt, M. A. (2024). Land use and land cover classes affected by the possible sea level rise in Mersin city center (Türkiye). Advanced GIS, 4(1), 15–23. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/1348