Optimization of Humanitarian Aid Distribution Using the Bee Colony Algorithm (#614)

Authors

Romero, Jeison

Cueva, Rony

Tupia, Manuel

Abstract

This study addresses the optimization of humanitarian logistics during natural disasters by implementing the Artificial Bee Colony (ABC) algorithm. Natural disasters frequently disrupt infrastructure and supply chains, complicating aid distribution. The paper reviews bio-inspired and metaheuristic algorithms, with a focus on ABC, which mimics the foraging behavior of bees to explore and exploit optimal solutions efficiently. The ABC algorithm is compared against a genetic algorithm using statistical tests, including Shapiro-Wilk, F-test, and Z-test, confirming ABC’s superior performance in resource distribution. The research identifies key challenges such as uncertain data, limited transportation capacity, and the need for equitable aid allocation. Numerical experimentation demonstrates that the ABC algorithm delivers higher efficiency and effectiveness in planning relief operations. Additionally, the study presents a software interface to facilitate parameter configuration and result visualization. This work contributes to the field by introducing the ABC algorithm in a new context, offering a reliable tool for disaster response planning.