Optimizing equity and efficiency in the aerial firefighting resource allocation problem

Abstract This study addresses the aerial firefighting resource allocation problem, formulated as the strategic prepositioning of a heterogeneous aerial firefighting fleet across capacity‐constrained airbases. A novel bi‐objective model is proposed that simultaneously optimizes efficiency (aggregate risk‐weighted accessibility) and equity (minimized regional service disparities). The model employs an enhanced spatial accessibility method with a distance‐decay function to evaluate coverage effectiveness and distribution fairness. A multi‐objective genetic algorithm is implemented to generate Pareto‐approximate solutions, revealing quantifiable trade‐offs. Applied to a national‐scale case study in Greece using 25 years of wildfire data, results demonstrate that the proposed framework provides significantly wider coverage than current practices, thereby extending protection to underserved regions while reducing redundant overlap. Sensitivity analyses confirm robustness across various real‐world conditions, such as different budget levels and aircraft availability. This transferable tool enables policymakers to make data‐driven decisions by quantifying the balance between response effectiveness and social fairness in wildfire‐prone regions.