Afet Lojistiği, İnsani Yardım Lojistiği ve Yardım Lojistiği Alanlarında Yapay Zekâ ve Makine Öğrenmesi Tekniklerinin Kullanımı: Sistematik Literatür Taraması

Abstract

Çalışmanın amacı afet lojistiği, insani yardım lojistiği ve yardım lojistiği alanlarında yapay zekâ ve makine öğrenmesi tekniklerinin kullanımına ilişkin literatürü incelemek ve araştırma eğilimleri ile boşlukları ortaya koymaktır. Web of Science (WoS) veri tabanında geçmişten 2025 yılı sonunu kapsayacak şekilde yapılan sistematik literatür taramasında 35 makaleye ulaşılmıştır. İki aşamalı analiz sürecinin ilk aşamasında makaleler anahtar kelime, yayın yılı, ülke ve odaklanılan temalar itibariyle analiz edilmiştir. İkinci aşamada makaleler daha detaylı incelenerek afetlerle ilgili hangi yapay zekâ ve makine öğrenmesi tekniklerinin hangi tip karar problemlerinde kullanıldığı belirlenmiştir. Bulgular afet lojistiğinde tahminleme, rotalama ve tahliye planlaması, ağ tasarımı ve dağıtım planlaması gibi problemler ile karar destek sistemlerinin oluşturulmasında yapay zekâ ve makine öğrenmesi tekniklerinden yoğun olarak faydalanıldığını göstermiştir. Çalışmanın; mevcut durumu ortaya koyarak afet lojistiği, insani yardım lojistiği ve yardım lojistiği alanlarında gelecekte yapılacak araştırmalar için yol gösterici olması beklenmektedir.

Keywords

• Afet lojistiği
• İnsani yardım lojistiği
• Yardım lojistiği
• Yapay zekâ
• Makine öğrenmesi

Thanks

Bu makale, Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü’nde Yasin Afşar tarafından, Prof. Dr. Arzum Büyükkeklik danışmanlığında ve Doç. Dr. Ahmet Şakir Dokuz ikinci danışmanlığında hazırlanan doktora tezinden üretilmiştir.

The Use of Machine Learning and Artificial Intelligence Techniques in Disaster Logistics, Humanitarian Logistics, Relief Logistics: Systematic Literature Review

Abstract

The aim of this study is to review the existing literature on the use of artificial intelligence and machine learning techniques in disaster logistics, humanitarian logistics, and relief logistics, and to identify prevailing research trends and gaps. In a systematic literature review conducted using the Web of Science (WoS) database covering the period from its inception through the end of 2025, a total of 35 articles were identified. In the first stage of the two-phase analysis process, the articles were analyzed in terms of keywords, publication year, country, and thematic focus. In the second stage, the articles were examined in greater depth to determine which artificial intelligence and machine learning techniques were applied to which types of disaster related decision problems. The findings indicate that artificial intelligence and machine learning techniques are extensively used in addressing problems such as disaster related forecasting, routing and evacuation planning, network design, and distribution planning, as well as in the development of decision support systems. By presenting the current state of the literature, this study is expected to provide guidance for future research in the fields of disaster logistics, humanitarian logistics, and relief logistics.

Keywords

• Disaster logistics
• Humanitarian logistics
• Relief logistics
• Artificial intelligence
• Machine learning

References

1. AFAD, 2021. Açıklamalı Afet Yönetimi Terimleri Sözlüğü, T.C. İçişleri Bakanlığı Afet ve Acil Durum Yönetimi Başkanlığı Erişim adresi: https://www.afad.gov.tr/aciklamali-afetyonetimi-terimleri-sozlugu
2. Ağdaş M., Bali Ö., Ballı H., 2014. Afet Lojistiği Kapsamında Dağıtım Merkezi İçin Yer Seçimi: Smaa-2 Tekniği İle Bir Uygulama, Beykoz Akademi Dergisi, 2(1), 75-94, https://doi.org/10.14514/BYK.m.21478082.2014.2/1.75-95
3. Alban A., Blaettchen P., Vries H., Wassenhove L.N.V., 2021. Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption, Manufacturing & Service Operations Management, 24(6), 2944-2961.
4. Amani M.A., Sarkodie S.A., Sheu J.B., Nasiri M.M., Tavakkoli-Moghaddam R., 2025. A data-driven hybrid scenario-based robust optimization method for relief logistics network design, Transportation Research Part E: Logistics and Transportation Review, 194, 103931.
5. Angin C., 2024. Artificial Intelligence in Disaster Management: Approaches, Methods and Applications, Turk Deprem Arastirma Dergisi, 6(2), 610-627, https://doi.org/10.46464/tdad.1532261
6. Aretoulaki E., Ponis S.T., Plakas G., 2024. Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform, Applied Sciences, 14(15), 6464.
7. Aringhieri R., Bigharaz S., Duma D., Guastalla A., 2022. Fairness in ambulance routing for post disaster management, Cent Eur J Oper Res, 30, 189-211.
8. Biswas S., Kumar D., Hajiaghaei-Keshteli M., Kumar Bera U., 2024. An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye, International Journal of Disaster Risk Reduction, 102(104287).

9. Chai R., He H., Liu D., Chen J., 2024. A fuzzy multi-objective programming model for the delivery and distribution of humanitarian relief materials, Engineering Applications of Artificial Intelligence, 137, Part A, 109113.
10. Chang K.H., Chen T.L., 2024. Simulation Learning and Optimization: Methodology and Applications, Asia-Pacific Journal of Operational Research, 2440008.
11. Chin T., Cheng T., Wang C., Huang L., 2024. Combining artificial and human intelligence to manage cross-cultural knowledge in humanitarian logistics: a Yin–Yang dialectic systems view of knowledge creation, Journal of Knowledge Management, 28(7), 1963-1977.
12. Danach K., Harb H., Saker L., Raad A., 2025. Quantum-Inspired Hyperheuristic Framework for Solving Dynamic Multi-Objective Combinatorial Problems in Disaster Logistics, World Electric Vehicle Journal, 16(6), 310.
13. Danişman Ş., Yalçın M., Çiftçi Ş.K., Tosuntaş Ş.B., Sölpük N., Ay Y., Karadağ E., Yücel E., 2016. Türkiye’de eğitim bilimleri alanında yayımlanan dergilerin bilimsel yayın haritası: Dergi etki faktörleri üzerine bir inceleme, Kuram ve Uygulamada Eğitim Yönetimi, 22(4), 483-506.
14. Diaz H.L., Imitola K.A., Amado R.J.A., 2019. OR/MS research perspectives in disaster operations management: a literature review, Facultad de Ingeniería, Universidad de Antioquia, 91, 43-59.
15. El Baz J., Laguir I., Stekelorum R., 2019. Logistics and Supply Chain Management Research in Africa: A Systematic Literature Review and Research Agenda, International Journal of Logistics Management, 30(1), 8-38.
16. EM-DAT, 2025. Monitoring hazards & disasters worldwide since 1988, The International Disaster Database Centre for Research on the Epidemiology of Disasters (EM-DAT), Erişim adresi: https://www.emdat.be/
17. Farazmehr S., Wu Y., 2023. Locating and deploying essential goods and equipment in disasters using AI-enabled approaches: A systematic literature review, Progress in Disaster Science, 19, 100292.
18. Fuqua D., Hespeler S., 2022. Commodity demand forecasting using modulated rank reduction for humanitarian logistics planning, Expert Systems with Applications, 206, 117753.
19. Galera-Zarco C., Floros G., 2024. A deep learning approach to improve built asset operations and disaster management in critical events: an integrative simulation model for quicker decision making, Ann Oper Res, 339, 573-612, https://doi.org/10.1007/s10479-023-05247-z
20. Guo P., Zhu J., 2023. Capacity reservation for humanitarian relief: A logic-based Benders decomposition method with subgradient cut, European Journal of Operational Research, 311(3), 942-970.
21. Islam S., Goerlandt F., Feng X., Uddin M.J., Shi Y., Hilliard C., 2020. Improving disasters preparedness and response for coastal communities using AIS ship tracking data, International Journal of Disaster Risk Reduction, 51, 101863.
22. Jmal S., Haddar B., Chabchoub. H., 2025. A Guided Quantum Particle Swarm Optimization approach for the traveling repairman problem, Journal of Industrial and Management Optimization, 2025, 21(7), 4753-4786.
23. Khan M., Parvaiz G.S., Ali A., Jehangir M., Hassan N., Bae J., 2022. A Model for Understanding the Mediating Association of Transparency between Emerging Technologies and Humanitarian Logistics Sustainability, Sustainability 14, 6917.
24. Kim J.D., Doh H.H., 2025. Backward Elimination Based on an Ensemble of Spare Parts to Forecast Initial Provisioning Demands, Military Operations Research, 30(2), 69–82, JSTOR, https://www.jstor.org/stable/27397118
25. Köstepen Z.N., Selim H., 2025. Literature Review on Humanitarian Logistics in Disaster Management: A Risk-Oriented Approach, Sustainability, 17(21), 9773.
26. Li D., Zhang Z., Alizadeh B., Zhang Z., Duffield N., Meyer M.A., Behzadan A.H., 2024. A reinforcement learning-based routing algorithm for large street networks, International Journal of Geographical Information Science, 38(2), 183-215.
27. Li C., Zhang F., Cao C., Liu Y., Qu T., 2019. Organizational Coordination in Sustainable Humanitarian Supply Chain: An Evolutionary Game Approach, Journal of Cleaner Production, 219, 291-303.
28. Menezes A., Wanke P., Antunes J., Pimenta R., Frare I., Andrade A., Oliveira W., Mamede A., 2024. Socio-Economic Impact of the Brumadinho Landslide: A Hybrid MCDM-ML Approach, Sustainability, 16(18), 8187.
29. Mengüç K., Aydin N., Ulu M., 2023. Optimisation of COVID-19 vaccination process using GIS, machine learning, and the multi-layered transportation model, International Journal of Production Research, 1-14.
30. Nabavi S.M., Vahdani B., Afshar-Nadjafi B., Adibi M.A., 2022. Synchronizing victim evacuation and debris removal: A data-driven robust prediction approach, European Journal of Operational Research, 300(2), 689-712.
31. Noyan N., Rudolf G., Lejeune M., 2021. Distributionally Robust Optimization Under a Decision-Dependent Ambiguity Set with Applications to Machine Scheduling and Humanitarian Logistics, INFORMS Journal on Computing, 34(2), 729-751.
32. Nurre S.G., Sharkey T.C., 2014. Integrated network design and scheduling problems with parallel identical machines: Complexity results and dispatching rules, Networks, 63: 306-326.
33. Ogbuke N., Yusuf Y.Y., Gunasekaran A., Wang X., 2025. Humanitarian supply chain operations and COVID-19: the roles of data-driven innovations, Production Planning & Control.
34. Pan J.S., Song P.C., Chu S.C., Snášel V., Watada J., 2025. Machine Learning-Enabled Evolutionary Two-Stage Stochastic Programming, IEEE Transactions on Emerging Topics in Computational Intelligence (early access).
35. Pantiris P., Pallis P.L., Chountalas P.T., Dasaklis T.K., 2025. Enhancing Coordination and Decision Making in Humanitarian Logistics Through Artificial Intelligence: A Grounded Theory Approach, Logistics, 9(3), 113.
36. Paul J., Criado A.R., 2020. The Art of Writing Literature Review: What Do We Know and What Do We Need to Know?, International Business Review, 29, 101717.
37. Racette M.P., Smith C.T., Cunningham M.P., Heekin T.A., Lemley J.P., Mathieu R.S., 2014. Improving Situational Awareness for Humanitarian Logistics Through Predictive Modeling, IEEE Systems and Information Engineering Design Symposium (SIEDS), 334-339.
38. Rodríguez-Espíndola O., Chowdhury S., Beltagui A., Albores P., 2020. The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, Artificial Intelligence and 3D printing, International Journal of Production Research, 58(15), 4610-4630.
39. Sánchez-Partida D., Bernábe Loranca M.B., Ruiz-Vanoye J.A., Barrera Camara R.A., 2024. Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques, International Journal of Combinatorial Optimization Problems and Informatics, 15(3), 28-42.
40. Thomas A.S., Kopczak L., 2005. From Logistics to Supply Chain Management: The Path Forward in the Humanitarian Sector, Fritz Institute, California, 15. 1-15.
41. Trejos C.A.R., Meisel J.D., Jaimes W.A., Cabrera J.P.O., 2025. Repair resources scheduling for attention of transitory road disruptions in humanitarian aid networks, Computers & Industrial Engineering, 203, 111020.
42. Torraco R.J., 2016. Writing Integrative Literature Reviews: Using the Past and Present to Explore the Future: Using the Past and Present to Explore the Future, Human Resource Development Review, 15(4), 404-428.
43. UNDDR, 2024. Sendai Framework Terminology on Disaster Risk Reduction, Erişim Adresi: https://www.undrr.org/terminology/disaster
44. Veloso R., Cespedes J., Caunhye A., Alem D., 2022. Brazilian disaster datasets and real-world instances for optimization and machine learning, Data in Brief, 42, 108012, ISSN 2352-3409.
45. Wang C., Lim M.K., Lyons A., 2019. Twenty Years of the International Journal of Logistics Research and Applications: A Bibliometric Overview, International Journal of Logistics Research and Applications, 22(3), 304-323.
46. Yıldız A., 2022. Bir Araştırma Metodolojisi Olarak Sistematik Literatür Taramasına Genel Bakış, Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22 (Özel Sayı 2), 367-386.
47. Zhao M., Liu X., 2018. Development of decision support tool for optimizing urban emergency rescue facility locations to improve humanitarian logistics management, Safety Science, 102, 110-117.
48. Zhu X., Zhang G., Sun B., 2019. A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence, Nat Hazards, 97, 65-82.