Multi-objective optimization of airport check-in counter allocation using genetic algorithms
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Publication date
2025
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04B - Conference paper
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2025 6th International Conference on Machine Learning and Human-Computer Interaction (MLHMI)
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114-119
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IEEE
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Kawasaki
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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.
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6th International Conference on Machine Learning and Human-Computer Interaction
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979-8-3315-3573-5
979-8-3315-3574-2
979-8-3315-3574-2
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English
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Yes
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Published
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peer-reviewed
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Citation
Puthuparambil, B., Hanne, T., & Dornberger, R. (2025). Multi-objective optimization of airport check-in counter allocation using genetic algorithms. 2025 6th International Conference on Machine Learning and Human-Computer Interaction (MLHMI), 114–119. https://doi.org/10.1109/MLHMI66056.2025.00024