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

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.