A Novel Rescheduling Algorithm for the Airline Recovery with Flight Priorities and Airport Capacity Constraints

Ji, Chenlu; Gao, Mingang; Zhang, Xu; Li, Jiaxuan

doi:10.1142/s021759592140025x

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Tian et al ( 31 ) suggested an enhanced column generation method to optimize the scheduling of disrupted flights, reducing the airline’s economic loss while boosting operational efficiency and providing a foundation for decision making. Ji et al ( 32 ) introduced a built-in flight feasibility verification technique to calculate the flight timetable in disruption management based on the lexicographic preference of flight priority.…”

Section: Literature Reviewmentioning

confidence: 99%

Reassigning Departure Slots with Preferences of the Airline and Passengers

Chen

Yang

2023

Transportation Research Record: Journal of the Transportation R

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The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline’s operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.

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Section: Literature Reviewmentioning

confidence: 99%

Reassigning Departure Slots with Preferences of the Airline and Passengers

Chen

Yang

2023

Transportation Research Record: Journal of the Transportation R

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“…Liang considered the aircraft recovery problem with airport capacity constraints and maintenance flexibility, and proposed a column generation heuristic method to solve it [15][16][17][18][19]. Ji considered the flight priority and airport capacity constraints and proposed a built-in flight feasibility verification algorithm to improve the rescheduling algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

A Study on Disrupted Flight Recovery Based on Logic-Based Benders Decomposition Method

Peng,

Hu,

Xia

2024

Aerospace

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Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into a master problem and sub-problems. The algorithm uses MIP in the master problem to determine flight cancellations and aircraft replacements. In the sub-problems, MIP or CP is used to determine the departure time of delayed flights. Later, incorporating sectional constraints into the main problem and iterating until an optimal solution is obtained. Furthermore, the added cutting plane constraint in the iterations of the Benders decomposition algorithm are strengthened to eliminate more inferior solutions. By comparing the results of CPLEX, the Logic-Based Benders decomposition algorithm, and the enhanced Benders decomposition algorithm, it is verified that the improved Benders decomposition algorithm can solve large-scale examples more efficiently with a faster time and fewer iterations.

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“…Aircraft pose estimation is a fundamental and challenging vision task that is crucial for many downstream tasks, such as intelligent airport security monitoring [ 1 ], decreasing aircraft crashes with airport surfaces [ 2 , 3 , 4 ], assisting subsequent airport control decisions [ 5 , 6 ], and establishing digital twin airports [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface

Yuan

Han

2023

Sensors

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The introduction of various deep neural network architectures has greatly advanced aircraft pose estimation using high-resolution images. However, realistic airport surface monitors typically take low-resolution (LR) images, and the results of the aircraft pose estimation are far from being accurate enough to be considered acceptable because of long-range capture. To fill this gap, we propose a brand-new, end-to-end low-resolution aircraft pose estimate network (LRF-SRNet) to address the problem of estimating the pose of poor-quality airport surface surveillance aircraft images. The method successfully combines the pose estimation method with the super-resolution (SR) technique. Specifically, to reconstruct high-resolution aircraft images, a super-resolution network (SRNet) is created. In addition, an essential component termed the large receptive field block (LRF block) helps estimate the aircraft’s pose. By broadening the neural network’s receptive field, it enables the perception of the aircraft’s structure. Experimental results demonstrate that, on the airport surface surveillance dataset, our method performs significantly better than the most widely used baseline methods, with AP exceeding Baseline and HRNet by 3.1% and 4.5%.

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A Novel Rescheduling Algorithm for the Airline Recovery with Flight Priorities and Airport Capacity Constraints

Cited by 6 publications

References 26 publications

Reassigning Departure Slots with Preferences of the Airline and Passengers

Reassigning Departure Slots with Preferences of the Airline and Passengers

A Study on Disrupted Flight Recovery Based on Logic-Based Benders Decomposition Method

LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface

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