A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
To address the issues of inefficient resource allocation and severe ground congestion at hub airports during aircraft turnaround operations, a two-stage optimization model is constructed to coordinate the scheduling of stands and ground support vehicles. The model focuses on analyzing the scheduling...
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Veröffentlicht in: | Applied sciences 2024-12, Vol.14 (23), p.11407 |
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Sprache: | eng |
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Zusammenfassung: | To address the issues of inefficient resource allocation and severe ground congestion at hub airports during aircraft turnaround operations, a two-stage optimization model is constructed to coordinate the scheduling of stands and ground support vehicles. The model focuses on analyzing the scheduling rules, operational patterns, and collaborative mechanisms between stand allocation and ground support vehicles, taking into account the coupling relationship between airport operational and support resources. The pre-allocation of stands is conducted under the constraints of limited support resources, and the results are used as inputs for ground support vehicle scheduling. This combined optimization of stands and vehicle resources enhances the overall resource efficiency. The NSGA-II algorithm, combining local search strategies (LS-NSGA-II), is used to solve the model. Computational experiments conducted at Shenzhen Airport show some improvements: For the stand allocation model, the model incorporates ground support service constraints for tow tractors and driving distances for ferry buses, thereby avoiding potential service conflicts and resource wastage. Secondly, for the scheduling of vehicles, by analyzing the operational patterns and service characteristics of different vehicles, the model improved vehicle utilization efficiency by 37.5%, reduced travel distance by 20.4%, and decreased waiting times by 57.6%, compared to the first-come-first-served strategy currently employed at airports. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app142311407 |