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Journals / Verimlilik Dergisi / 2026 / Cilt: 60 Sayı: 1

AI-Enhanced Digital Twin Framework for Humanitarian Logistics: a Decision Support Approach for Crisis Response

İnsani Lojistik için Yapay Zekâ Destekli Dijital İkiz Çerçevesi: Kriz Müdahalesi için Bir Karar Destek Yaklaşımı

Musab Talha Akpınar(ANKARA YILDIRIM BEYAZIT UNIVERSITY)0000-0003-4651-7788
Pages
21–38
Publication Date
2026-01-28
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AI Summary & Key Takeaways

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

Originality: This research pioneers a federated AI-driven Digital Twin model for humanitarian logistics, enabling collaborative, data-driven crisis management with innovative hybrid modeling techniques.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Purpose: This study aims to develop an AI-enhanced Digital Twin framework (HLDT) for humanitarian logistics, addressing the research question: How can AI-driven Digital Twins improve decision-making and efficiency in crisis response?
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Key Findings & Data:Methodology: A qualitative approach was employed, conducting semi-structured interviews with 15 humanitarian logistics experts to identify challenges and inform the design of the DSA-HLDT model.
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Conclusion & Impact:Originality: This research pioneers a federated AI-driven Digital Twin model for humanitarian logistics, enabling collaborative, data-driven crisis management with innovative hybrid modeling techniques.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İnsani Yardım · Yapay Zekâ (YZ) · Dijital İkiz

Abstract

Purpose: This study aims to develop an AI-enhanced Digital Twin framework (HLDT) for humanitarian logistics, addressing the research question: How can AI-driven Digital Twins improve decision-making and efficiency in crisis response? The scope focuses on optimizing operations, resource allocation, and transparency in disaster scenarios. Methodology: A qualitative approach was employed, conducting semi-structured interviews with 15 humanitarian logistics experts to identify challenges and inform the design of the DSA-HLDT model. The framework integrates real-time data, AI analytics, and simulation, validated using real-world datasets from organizations like the United Nations and Red Cross. Findings: The HLDT framework significantly reduced response times, optimized resource allocation, and improved multi-agency coordination. Blockchain and IoT integration enhanced operational transparency, ensuring efficient aid distribution. Originality: This research pioneers a federated AI-driven Digital Twin model for humanitarian logistics, enabling collaborative, data-driven crisis management with innovative hybrid modeling techniques.

Özet

Amaç: Bu çalışma, insani lojistikte YZ destekli bir Dijital İkiz çerçevesi (HLDT) geliştirerek şu soruyu yanıtlamayı amaçlar: YZ tabanlı Dijital İkizler, kriz müdahalesinde karar alma ve verimliliği nasıl iyileştirebilir? Kapsam, afet senaryolarında operasyonları, kaynak tahsisini ve şeffaflığı optimize etmeye odaklanır. Yöntem: Nitel bir yaklaşım benimsenmiş, 15 insani lojistik uzmanıyla yarı yapılandırılmış görüşmeler yapılarak zorluklar belirlenmiş ve DSA-HLDT modeli tasarlanmıştır. Çerçeve, gerçek zamanlı veri, YZ analizleri ve simülasyonu entegre etmiş; Birleşmiş Milletler ve Kızılhaç verileriyle doğrulanmıştır. Bulgular: HLDT çerçevesi, yanıt sürelerini önemli ölçüde azaltmış, kaynak tahsisini optimize etmiş ve çoklu ajanslar arası koordinasyonu güçlendirmiştir. Blockchain ve IoT entegrasyonu, operasyonel şeffaflığı artırarak etkin yardım dağıtımı sağlamıştır. Özgünlük: Bu araştırma, federated YZ tabanlı bir Dijital İkiz modeliyle insani lojistikte işbirlikçi, veri odaklı kriz yönetimi sunarak yenilikçi bir hibrit modelleme yaklaşımı getirmektedir.

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Cite This Article

Akpınar, M. T. (2026). AI-Enhanced Digital Twin Framework for Humanitarian Logistics: a Decision Support Approach for Crisis Response. Verimlilik Dergisi, Cilt: 60 Sayı: 1, 21–38. https://doi.org/10.51551/verimlilik.1685350

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