Multi-objective optimization of airport check-in counter allocation using genetic algorithms

Abstract

This paper presents a multi-objective optimization approach using Genetic Algorithms (GAs) to address the Airport Check-In Counter Allocation problem. A hybrid model balancing operational costs, passenger waiting times, resource utilization, and service levels is developed. A GA framework, implemented with the DEAP library in Python, evaluates multiple scenarios through various test cases to assess performance under different conditions. The results demonstrate the robustness and adaptability of GAs in achieving high resource utilization and service levels with up to zero waiting times, even under increased demand and varying parameter settings. This study highlights the potential of GAs for solving complex multi-objective optimization problems in dynamic environments and suggests future research directions, including hybrid optimization methods and diverse parameter settings.