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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Research and Scientific Innovation Society
2025
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/29629/2/00197230120261043552949.pdf http://eprints.utem.edu.my/id/eprint/29629/ https://rsisinternational.org/journals/ijriss/uploads/vol9-iss11-pg7932-7941-202512_pdf.pdf https://doi.org/10.47772/IJRISS.2025.91100618 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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-FirstServe (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. |
|---|