Flights Assignment Model of Multiple Airports Based on Game Theory and CDM Mechanism

Liu, Junqiang

doi:10.1155/2020/2569280

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…As for constraint (5), it stipulates that flights can only undergo de-icing operations in one de-icing position at any given time. Moreover, constraints (7) to (9) outline the relationships among the earliest feasible de-icing pad entry time, the de-icing duration for Type K flights, and the actual departure time of flights, encompassing various time variables. These equations lay the groundwork for calculating the time constraints in the upper-level model, denoted as constraint (6).…”

Section: Upper-level Modeling-aircraft Scheduling Problem With Finite...mentioning

confidence: 99%

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Liu,

Huang,

Han

et al. 2024

Biomimetics

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Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach’s efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.

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Section: Upper-level Modeling-aircraft Scheduling Problem With Finite...mentioning

confidence: 99%

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Liu,

Huang,

Han

et al. 2024

Biomimetics

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“…Air transport is currently operated through a network. The airport network is complex and consists of mutually interacting and correlated aviation transport resources including airports, routes, transport capacity, and airspace resources [3]. Airports and the routes connecting them form the basic structure of the airport network.…”

Section: Introductionmentioning

confidence: 99%

Critical and steady-state characteristics of delay propagation in an airport network

Yao

Zhang

2023

PLoS ONE

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In this work, we established a density equation for delayed airports to investigate the horizontal propagation mechanism of delays among airports in an airport network. We explored the critical conditions, steady-state features, and scale of the delay propagation, and designed a simulation system to verify the accuracy of the results. The results indicated that, due to the no-table scale-free feature of an airport network, the critical value of delay propagation is extremely small, and delays are prone to propagate among airports. Furthermore, as delay propagation reaches a steady state in an aviation network, the degree value of the node becomes highly correlated with its delay state. Hub airports with high degree values are the most prone to being affected by delay propagation. In addition, the number of airports that are initially delayed influences the time required for delay propagation to reach a steady state. Specifically, if there are fewer initially delayed airports, a longer time is required to reach a steady state. In the steady state, the delay ratios of airports with different degree values in the network converge to a balance point. The delay degree of the node is highly positively correlated with the delay propagation rate in the network, but negatively related to the degree distribution index of the network.

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“…Erkan et al (2019) proposed a general model that could be used to rearrange incoming and outgoing flights under the CDM mechanism. Liu (2020) studied the problem of slot allocation under delayed conditions. Taking into account the cost of flight delays, airport capacity and flight transfers between airlines, he proposed a new allocation model based on game theory and CDM mechanism, and designed an improved one.…”

Section: Introductionmentioning

confidence: 99%

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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Flights Assignment Model of Multiple Airports Based on Game Theory and CDM Mechanism

Cited by 3 publications

References 22 publications

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Critical and steady-state characteristics of delay propagation in an airport network

Collaborative slot secondary allocation based on flight wave operation

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