Optimal scheduling of airport ferry vehicles based on capacity network

Han, Xue; Zhao, Peixin; Meng, Qingchun; Yin, Shengnan; Wan, Di

doi:10.1007/s10479-020-03743-0

Cited by 15 publications

(2 citation statements)

References 16 publications

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“…For ferry vehicle scheduling, Zhao et al [12] constructed a ferry vehicle sharing network, where the model was transformed into the problem of maximum network flow. Han et al [13] also proposed a ferry capacity network model, where the directed edge indicates that the two associated nodes may be continuously served by the same ferry. Li et al [14] investigated the ferry vehicle scheduling problem and established a quadratic programming model to minimize the variance in the number of flights per ferry vehicle serving.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

Ding

Xie

et al. 2022

Security and Communication Networks

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Airport ferry vehicles are used to transport passengers between the far apron and terminals. During the peak hours of flight arrival or departure, the demand for ferry vehicles will increase, and improper scheduling will result in significant delays. In this study, we construct a 0–1 integer programming scheduling model for ferry vehicles with multiple objectives: minimize flight delays and vehicle travel distances and balance vehicle workloads when there is an insufficient number of ferry vehicles. To solve the multiobjective model, this study selects the second version of the nondominated sorting genetic algorithm-II as the main framework and designs a modified coding and decoding scheme to solve the constraint problem in the model. The proposed model and algorithm are verified using actual data from Kunming Changshui International Airport, China. Compared with the scheduling method of manual first come first serve, the total delay time of the proposed model is reduced by 24.52%, the transit distance by 6.63%, and the balance index by 24.05%, which helps the airport solve the problem of insufficient ferry vehicles during peak hours. Comparison results show that the proposed model has a shorter calculation time and better distribution of solution sets than three state-of-the-art multiobjective optimization algorithms: MOPSO, MOACO, and SPEA-II. This research can help airports reduce flight delays, vehicle purchases, and operation costs.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

Ding

Xie

et al. 2022

Security and Communication Networks

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“…In terms of ferry vehicles, Zhao et al [9] established an integer programming model with the goal of minimizing the number of ferry vehicles required in peak hours or a certain period and constructed a ferry vehicle sharing network, which transformed the model into the problem of maximum network flow. Han et al [10] also proposed a ferry capacity network model, in which the directed edge indicates that the two associated nodes may be continuously served by the same ferry.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data

Ding

Xie

et al. 2021

Journal of Advanced Transportation

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An airport ferry vehicle is a ground service vehicle used to transfer passengers between the far apron and the terminal. The travel time of ferry tasks in the airport ferry network is an important decision-making basis for ferry vehicle scheduling. This paper presents a graph-based method to mine the travel time between nodes in the airport ferry network. Firstly, combined with map and trajectory information, the method takes the terminal boarding gates, parking lots, and remote stands as road network nodes to build a complete airport ferry road network. Then, this paper uses big data processing technology to identify the travel time between regional connection nodes by data fusion through the temporal and spatial relationship between flight schedule and ferry vehicle GPS travel trajectory. Finally, the Floyd shortest path algorithm in graph theory is used to obtain the shortest path and travel time of all OD points. The experimental results show that all the ferry times calculated by the method proposed in this paper can better reflect the actual driving situation. This method saves the manpower, material resources, and time cost of on-site investigation and lays a foundation for the scheduling of ferry vehicles.

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