Algorithmes génétiques appliqués à la gestion du trafic aérien

Durand, Nicolas; Gotteland, Jean-Baptiste

doi:10.1051/bib-j3ea:2003506

Cited by 9 publications

(5 citation statements)

References 8 publications

(5 reference statements)

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“…When several aircraft are involved in a conflict, the conflict resolution problem has been shown to be NPhard (Durand, 2004). Moreover, the optimization variables being the B-splines control-point location, we shall see that our objective function (4) is not differentiable with respect to these variables.…”

Section: Optimization Methodsmentioning

confidence: 94%

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Optimizing B-Splines Using Genetic Algorithms Applied to Air-Traffic Conflict Resolution

Peyronne

Delahaye

Mongeau

et al. 2010

Proceedings of the International Conference on Evolutionary Computation

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Abstract:Conflict resolution has always been a sensitive matter in air-traffic management. Current European projects aim partial or total automation of air traffic control to deal with the constant growth of air traffic. Technological advances on flight management system allows us to consider an automatic conflict resolution using continuous trajectories. In this paper, we present a new methodology that, first, relies on B-splines to model trajectories, secondly models air-traffic conflict resolution as an optimization problem whose decision variables are the spline control points. Finally, we use genetic algorithms to tackle this optimization problem in order to generate optimal conflict-free situations.

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Section: Optimization Methodsmentioning

confidence: 94%

“…By the past, two kinds of method have become predominant in automatic air traffic conflict resolution for their good results : genetic algorithms (GA) (Durand, 2004) and navigation functions (Zeghal, 1994).…”

Section: Previous Related Workmentioning

confidence: 99%

Optimizing B-Splines Using Genetic Algorithms Applied to Air-Traffic Conflict Resolution

Peyronne

Delahaye

Mongeau

et al. 2010

Proceedings of the International Conference on Evolutionary Computation

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“…Holland presented the genetic algorithm as an abstraction of biological evolution and proposed a theorical framework for adaption under the GA (Holland, 1975).The genetic algorithm consists of natural selection which imitates the natural genetic mechanism of biological organisms (Chowdhury & Garai, 2017). For achieving the optimal solution, the genetic algorithm repetitively changes a population of individual solutions (Durand, 2004). Therefore, the choice of the initial population is…”

Section: Genetic Algorithmmentioning

confidence: 99%

Modeling Financial Supply Chain Planning Under COVID-19 Conditions for Working Capital Optimization Through Genetic Algorithm

Semaa

Alaoui

Farhaoui

et al. 2022

International Journal of Applied Metaheuristic Computing

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The coordination of physical flows, information flows and financial flows is nowadays essential to guarantee economic profitability and ensure the company's sustainability. Time differences between physical operating flows and cash flows create financing needs within the company. These needs must be optimised at the same time as logistical decisions and can have a considerable impact on the company's overall performance. Recently, with the Coronavirus disease 2019 crisis and the period of containment, many companies are suffering from a lack of liquidity due to the closure of certain companies. While considering this health crisis, we will design two mathematical models: one before containment and the second after containment. In the latter, we will propose a solution to the payment problem in order to improve working capital performance. We use the genetic algorithm to solve the two mathematical models, and we run simulations first on a real dataset from a dairy cooperative and subsequently on a second dataset available online.

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“…The remaining parameters with a high Sobol index are not considered, since they would be rather related to Calcium fluxes. We choose to apply a genetic optimization algorithm [6] to fit these parameters to the data, because of the high dimensionality of the problem. The selection process at each iteration is based on minimizing y sim − y obs l 2 , where y obs is the experimental normalized oxygen concentration, and y sim is the simulated one.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Global Sensitivity Analysis for Uncertain Parameters Applied to a Cardiac Mitochondrial Model

Tarraf¹,

Leguèbe²,

Coudière³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Cardiac mitochondria are intracellular organelles that have several important roles. For instance, they ensure energy metabolism and calcium regulation, thus are linked to the excitation-contraction cycle of the heart cell. Mathematical models are useful to better understand the complexity of mitochondrial dynamics within a cardiac cell, and we are specifically interested in the dynamics of calcium. Litterature models reflect this complexity, especially in terms of number of equations and parameters, which makes them impossible to calibrate to experimental data.In this paper, we apply a global sensitivity analysis on our previously discussed simple mitochondria model [1], in order to quantify the uncertainty of the parameters. This analysis is done in two steps. First we eliminate noninfluential parameters of the internal fluxes governing the activity of the mitochondria. Then we perform this analysis on the outputs of our ODE (ordinary differential equation) model, which are the respiratory rates.Finally, we calibrate the remaining influential parameters using a genetic algorithm with respect to experimental respiratory data.

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Algorithmes génétiques appliqués à la gestion du trafic aérien

Cited by 9 publications

References 8 publications

Optimizing B-Splines Using Genetic Algorithms Applied to Air-Traffic Conflict Resolution

Optimizing B-Splines Using Genetic Algorithms Applied to Air-Traffic Conflict Resolution

Modeling Financial Supply Chain Planning Under COVID-19 Conditions for Working Capital Optimization Through Genetic Algorithm

Global Sensitivity Analysis for Uncertain Parameters Applied to a Cardiac Mitochondrial Model

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