Heuristic Scheduling Strategies for the Airport Check-In Counter Allocation Problem

The post-pandemic resurgence in global air travel has placed renewed strain on airport infrastructure, establishing the check-in hall as a critical bottleneck for operational efficiency and passenger satisfaction. This study addresses the Airport Check-in Counter Allocation Problem (CCAP) within the specific context of Malaysian airports, proposing a robust heuristic scheduling framework to mitigate resource congestion. By integrating rule-of-thumb heuristics with fundamental dispatching algorithms, specifically First-Come-First-Serve (FCFS), Earliest Deadline First (EDF), and Shortest Job First (SJF). The research employs a discrete simulation to evaluate performance under two contrasting regulatory environments: a flexible Mixed Counter strategy and a stringent Preferred Counter policy. The comparative analysis reveals that the Heuristic-FCFS combination under flexible allocation rules yields the optimal outcome, achieving a peak resource utilization rate of 45.3% while minimizing idle dormancy. Conversely, the enforcement of airline-specific constraints resulted in significant resource fragmentation, necessitating a 35% increase in active counters and depressing utilization rates to approximately 33.5% across all algorithmic variants. These findings provide empirical evidence that while algorithmic optimization contributes to efficiency, the structural removal of categorical resource barriers offers the most significant potential for economic and operational improvement in airport management.

Airport Check-in Counter Allocation; Heuristic Scheduling Algorithms

1. F. Zhang and D. J. Graham, “Air transport and economic growth: a review of the impact mechanism and causal relationships,” Transport Reviews, vol. 40, no. 4, pp. 506–528, 2020. [Google Scholar] [Crossref]

2. L. Adacher and M. Flamini, "Optimization of airport check-in service quality focused on operational costs and passengers' satisfaction," PLOS ONE, vol. 16, no. 8, e0253586, 2021. [Google Scholar] [Crossref]

3. T. R. Lalita and G. S. R. Murthy, “The Airport Check-in Counter Allocation Problem: A Survey,” arXiv preprint arXiv:2208.13544, 2022. [Google Scholar] [Crossref]

4. M. A. Ornek, C. Ozturk, and I. Sugut, “Model-based heuristic for counter assignment problem with operational constraints,” Journal of Air Transport Management, vol. 87, 2020. [Google Scholar] [Crossref]

5. Y. Wang, "Dynamic provisioning of airport resources for inbound passenger flow using reinforcement learning," Transportation Research Part C: Emerging Technologies, vol. 148, 2023. [Google Scholar] [Crossref]

6. N. A. Ribeiro, A. Jacquillat, and A. R. Odoni, "An optimization approach for airport slot allocation under IATA guidelines," Transportation Research Part B, vol. 140, 2021. [Google Scholar] [Crossref]

7. K. Sangaiah, et al., “IoT Resource Allocation and Optimization Based on Heuristic Algorithm,” Sensors, vol. 20, no. 2, p. 539, 2020. [Google Scholar] [Crossref]

8. K. Li, G. Xu, and Y. Dong, "Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization," IEEE Transactions on Cloud Computing, vol. 12, no. 3, 2022. [Google Scholar] [Crossref]

9. G. Gupta, V. Kumawat, and D. Singh, “A simulation of priority based earliest deadline first scheduling,” in 2021 International Conference on Networks & Soft Computing, IEEE, 2021. [Google Scholar] [Crossref]

10. J. Singh and B. Patra, "An Algorithm to Reduce the Time Complexity of Earliest Deadline First Scheduling," International Journal of Advanced Computer Science, vol. 14, no. 2, 2023. [Google Scholar] [Crossref]

11. V. Karthick, E. Ramaraj, and R. G. Subramanian, “An Efficient Multi Queue Job Scheduling for Cloud Computing,” in 2021 International Conference on Computing, IEEE, 2021, pp. 164–166. [Google Scholar] [Crossref]

12. S. J. Patel and U. R. Bhoi, “Improved Priority Based Job Scheduling Algorithm Using Iterative Method,” Advances in Computing and Communications, vol. 10, 2024. [Google Scholar] [Crossref]

13. S. Otieno, “Optimizing Airport Operations: A Study on Passenger Flow and Terminal Efficiency,” OTS Canadian Journal, vol. 4, no. 6, 2025. [Google Scholar] [Crossref]

14. Brun, E. Feron, S. Alam, and D. Delahaye, "Schedule optimization and staff allocation for airport security checkpoints using guided simulated annealing," Journal of Air Transport Management, vol. 124, 2025. [Google Scholar] [Crossref]

15. DLR, "Post-Pandemic Impact Analysis for Airport Processes From Security to Boarding," German Aerospace Center (DLR) Research Report, 2022. [Google Scholar] [Crossref]

• The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
• Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
• A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
• The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
• Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria