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Journals / Advances in Artificial Intelligence Research / 2026 / Cilt: 6

An Artificial Intelligence Supported Decision Support System for Multi Criteria Rescuer Assignment in Disaster Response

Afet Müdahalesinde Çok Kriterli Kurtarıcı Ataması için Yapay Zeka Destekli Karar Destek Sistemi

Nurettin Havutçu(SULEYMAN DEMIREL UNIVERSITY)0000-0002-7840-3593
Mevlüt Ersoy(SULEYMAN DEMIREL UNIVERSITY)0000-0003-2963-7729
Pages
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Publication Date
2026-08-07
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AI Summary & Key Takeaways

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

The principal finding is that the incremental solution (Φ = 0.8471) yields a higher objective value than the static approach solving the same data in a single pass (Φ = 0.8255), showing that the locked field state preserves operational continuity without sacrificing solution quality.

📌 3 Key Takeaways

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Objective & Hypothesis:The assignment of a limited number of search and rescue (SAR) personnel to multiple, geographically dispersed disaster sites is a critical decision problem that directly determines the effectiveness of the initial response.
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Key Findings & Data:In this study, the problem is modeled around a unified objective function (Φ) that integrates personnel competence, travel proximity, disaster demand, coverage ratio, and operational team cohesion.
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Conclusion & Impact:The principal finding is that the incremental solution (Φ = 0.8471) yields a higher objective value than the static approach solving the same data in a single pass (Φ = 0.8255), showing that the locked field state preserves operational continuity without sacrificing solution quality.
🔬Design:Modeling & Simulation
📍Scope:2023 Verileri
⚙️Variables:Afet yönetimi · Arama ve kurtarma · Karar destek sistemi

Abstract

The assignment of a limited number of search and rescue (SAR) personnel to multiple, geographically dispersed disaster sites is a critical decision problem that directly determines the effectiveness of the initial response. Although this problem extends the classical assignment problem, the multidimensional nature of disaster operations cannot be adequately captured by single criterion distance minimization. In this study, the problem is modeled around a unified objective function (Φ) that integrates personnel competence, travel proximity, disaster demand, coverage ratio, and operational team cohesion. Under this common objective, Mixed Integer Linear Programming (LP/MILP) and four nature-inspired metaheuristics (Grey Wolf Optimizer, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization) are evaluated within a fair comparison framework. The method's dynamic incremental data mechanism also allows for the addition of reinforcement personnel arriving after the initial assignment and newly reported crash areas, while previously applied assignments remain locked. The method is validated on an urban earthquake scenario for the Çukurova district of Adana province, inspired by the 2023 Kahramanmaraş earthquakes. Across four scenarios representing a gradual transition from initial response to full capacity containing three disaster types, a total of 600 runs are evaluated using descriptive statistics, non-parametric hypothesis tests (the Friedman test and the Nemenyi post-hoc test), and convergence and sensitivity analyses. The principal finding is that the incremental solution (Φ = 0.8471) yields a higher objective value than the static approach solving the same data in a single pass (Φ = 0.8255), showing that the locked field state preserves operational continuity without sacrificing solution quality.

Özet

Sınırlı sayıdaki arama ve kurtarma (AK) personelinin coğrafi olarak dağılmış birden fazla afet sahasına atanması, ilk müdahalenin etkinliğini doğrudan belirleyen kritik bir karar problemidir. Bu problem, klasik atama probleminin bir uzantısı olmakla birlikte, afet operasyonlarının çok boyutlu yapısı yalnızca tek ölçütlü mesafe minimizasyonu ile yeterli şekilde temsil edilememektedir. Bu çalışmada problem, personel yetkinliği, ulaşım yakınlığı, afet talebi, kapsama oranı ve operasyonel ekip uyumunu bütünleşik biçimde değerlendiren tek bir amaç fonksiyonu (Φ) etrafında modellenmiştir. Bu ortak amaç fonksiyonu altında, Karma Tamsayılı Doğrusal Programlama (LP/MILP) ile doğadan ilham alan dört meta sezgisel algoritma (Gri Kurt Optimizasyonu, Genetik Algoritma, Parçacık Sürü Optimizasyonu ve Karınca Kolonisi Optimizasyonu) adil bir karşılaştırma çerçevesinde değerlendirilmiştir. Yöntemin dinamik artımlı veri mekanizması, ilk atamadan sonra bölgeye ulaşan takviye personelin ve sonradan bildirilen yeni enkaz alanlarının sisteme eklenmesine olanak tanırken, daha önce gerçekleştirilen atamalar kilitlenerek korunmaktadır. Yöntem, 2023 Kahramanmaraş depremlerinden esinlenilerek Adana ili Çukurova ilçesi için oluşturulan kentsel deprem senaryosu üzerinde doğrulanmıştır. Üç farklı afet türünü içeren ve ilk müdahaleden tam kapasiteye kademeli geçişi temsil eden dört senaryo kapsamında toplam 600 çalıştırma; betimsel istatistikler, parametrik olmayan hipotez testleri (Friedman testi ve Nemenyi post-hoc testi) ile yakınsama ve duyarlılık analizleri kullanılarak değerlendirilmiştir. Elde edilen temel bulgu, artımlı çözüm yaklaşımının (Φ = 0,8471), aynı veri kümesinin tek seferde çözüldüğü statik yaklaşıma (Φ = 0,8255) kıyasla daha yüksek bir amaç fonksiyonu değeri ürettiğini göstermektedir. Bu sonuç, sahada uygulanmış atamaların kilitlenerek korunmasının operasyonel sürekliliği muhafaza ederken çözüm kalitesinden ödün vermediğini ortaya koymaktadır.

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Cite This Article

Havutçu, N., Ersoy, M. (2026). An Artificial Intelligence Supported Decision Support System for Multi Criteria Rescuer Assignment in Disaster Response. Advances in Artificial Intelligence Research, Cilt: 6. https://doi.org/10.54569/aair.1976837

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