Equity and Strength in Stochastic Integer Programming Models for the Dynamic Single Airport Ground-Holding Problem

Estes, Alexander; Ball, Michael O.

doi:10.1287/trsc.2020.0975

Cited by 10 publications

(3 citation statements)

References 6 publications

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Section: Introductionmentioning

confidence: 99%

“…Ball et al (2020) focused on auctions that allocated airport arrival and departure slots and proposed a continuous model and an integer programming model for the associated winner determination problem. Estes and Ball (2020) suggested a stochastic integer programming model for distributing delays to flights to capacity-constrained airports that not only preserves the strength of previous models but also it is more flexible for the problem of delay allocation for various flight sizes. Ribeiro et al (2018) proposed a novel multi-objective priority-based slot allocation model (PSAM) that optimized slot allocation, while complying with the complex set of priorities and requirements specified by the International Air Transport Association (IATA) guidelines, as well as with the declared capacity constraints at the airports.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative slot secondary allocation based on flight wave operation

Chen

Yang

et al. 2022

IJICC

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PurposeFlights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.Design/methodology/approachTaking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.FindingsComparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.Originality/valueBased on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative slot secondary allocation based on flight wave operation

Chen

Yang

et al. 2022

IJICC

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“…For instance, at the strategic level, optimize the airport surface and airspace structure or adopt advanced collaborative operation concepts at the tactical level to improve the operating efficiency of the entire airport and airspace network and reduce flight delays [2][3][4]. Besides, after flight delays, effective control measures (such as ground waiting) are taken to reduce the extent and scope of flight delays [5][6][7]. In this paper, we focus on the problem of airport delay prediction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction

Zeng

Quan

et al. 2021

Journal of Advanced Transportation

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Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports’ relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness.

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