Using Markov decision processes to solve stochastic gate assignment problem

Aoun, Oussama; Afia, Abdellatif El

doi:10.1109/gol.2014.6887445

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…In the case of Covid-19, Liu et al (2020) showed that mathematical methods provided more reliable results and more accurate estimates than those of stochastic and statistical models 11 . Although, stochastic modeling can be useful in numerous other study cases like in Aoun and El Afia (2014a, 2014b, 2018 and El Afia and Aoun (2017).…”

Section: Modelingmentioning

confidence: 99%

Modeling the spread of Covid-19 pandemic: case of Morocco

Lotfi

Aoun

2020

Epidemiologic Methods

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AbstractObjectiveThis paper is establishing the relationship between the spreading dynamics of the Covid-19 pandemic in Morocco and the efficiency of the measures and actions taken by public authorities to contain it. The main objective is to predict the evolution of the COVID-19 pandemic in Morocco and to estimate the time needed for its disappearance.MethodsFor these reasons, we have highlighted the role of mathematical models in understanding the transmission chain of this virus as well as its future evolution. Then we used the SIR epidemiological model, which proves to be well suited to address this issue. It shows that identification of the key parameters of this pandemic, such as the probability of transmission, should help to adequately explain its behaviour and make it easier to predict its progress.ResultsAs a result, the measures and actions taken by the public authorities in Morocco allowed to record lower number of virus reproduction than many countries.ConclusionSo, in the case of Morocco, we were able to predict that the Covid-19 pandemic should disappear in a shorter time and without registering a larger number of infected individuals compared to other countries.

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Section: Modelingmentioning

confidence: 99%

Modeling the spread of Covid-19 pandemic: case of Morocco

Lotfi

Aoun

2020

Epidemiologic Methods

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“…Stollenwerk et al [ 27 ] considered the transfer time of passengers and adopted an improved quantum annealing method to solve it. Aoun and EI Afia [ 28 ] solved the problem of aircraft position assignment under uncertain conditions based on the Markov decision process and used random parameters to represent fluctuations in flight operations. Another paper by these authors [ 29 ] took into account that aircraft could be delayed, established an allocation model based on a multi-agent Markov decision-making process, and expressed the stand as a collaborative agent trying to complete a series of flight assignment tasks, finally providing the staff with a robust priority solution.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem

Ding

Wang

2023

Entropy

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Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm.

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“…The MDP provided a robust solution for the GAP. 19 YH Wang et al 20 built a mixed set programming method to build a data and logical model of gate assignment and developed efficient search strategies to achieve global optimal solution. KC Chong et al proposed a model to minimize the total distance between the gates that are related to assign the arrival plane to the suitable gates.…”

Section: Reviewmentioning

confidence: 99%

Multi-objective gate assignment based on robustness in hub airports

Zhang

Xue

Jiang

2017

Advances in Mechanical Engineering

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To enhance the robustness of the gate assignment, reduce the possibility of flight conflict, and improve the quality of passenger services, a multi-objective gate assignment model is proposed by minimizing flight conflict probability and number of flights assigned to aprons. The biogeography-based optimization algorithm is used to solve the proposed model with a new method for estimating the conflict probability. The simulation results show that the ratio of interval time of two flights assigned to the same gate between 60 and 120 min is as high as 82% when the rate of the flights assigned to aprons is controlled below 0.4. This means that the robustness increases greatly, and the probability of flight conflicts decreases, which is beneficial to the implement of flight assignment plan. In addition, the biogeography-based optimization algorithm is more effective to solve the proposed model and very easy to find out the optimal solutions.

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Using Markov decision processes to solve stochastic gate assignment problem

Cited by 8 publications

References 14 publications

Modeling the spread of Covid-19 pandemic: case of Morocco

Modeling the spread of Covid-19 pandemic: case of Morocco

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem

Multi-objective gate assignment based on robustness in hub airports

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