Multiairport Capacity Management: Genetic Algorithm With Receding Horizon

Hu, Xiao-Bing; Chen, Wen‐Hua; Paolo, Ezequiel A. Di

doi:10.1109/tits.2006.890067

Cited by 33 publications

(13 citation statements)

References 16 publications

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“…The optimization approach is based on the implementation of the sliding window method. This method has been employed in other studies with good results [8], [30], [31], [35], [36], [37], [38]. The sliding window approach consists in considering time frame windows of small size and shifting them along the entire time horizon of the study.…”

Section: A Optimization Approachmentioning

confidence: 99%

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Scala

Mota

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Airport capacity has become a constraint in the air transportation networks, due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum the potential conflicts. In this framework, airspace together with ground airport operations are considered. Conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways, and as capacity overloads for taxiway network and terminals. The methodology proposed in this work consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, this is supported by a case study of Paris Charles de Gaulle Airport.

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Section: A Optimization Approachmentioning

confidence: 99%

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Scala

Mota

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…RHC method forms the closed-loop rolling mechanism, including observation, planning, implementation, and re-observe. RHC is a p-step-ahead online optimization strategy [15]. At each time interval, RHC optimizes the specific problem for the following p intervals based on current available information.…”

Section: Principle Of Rhcmentioning

confidence: 99%

“…Recently, RHC is utilized to achieve formation flight and other cooperative tasks [14], and the RHC-based method proves more successful in optimizing airport capacity profile in a dynamic environment [15]. It is based on the simple idea of repetitive solution of an optimal control problem and state updating after the first input of the optimal command sequence [16].…”

Section: Introductionmentioning

confidence: 99%

Receding horizon control for multi-UAVs close formation control based on differential evolution

Zhang¹,

Duan²,

Yuan³

2010

Sci. China Inf. Sci.

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Close formation flight is one of the most complicated problems on multi-uninhabited aerial vehicles (UAVs) coordinated control. Based on the nonlinear model of multi-UAVs close formation, a novel type of control strategy of using hybrid receding horizon control (RHC) and differential evolution algorithm is proposed. The issue of multi-UAVs close formation is transformed into several on-line optimization problems at a series of receding horizons, while the differential evolution algorithm is adopted to optimize control sequences at each receding horizon. Then, based on the Markov chain model, the convergence of differential evolution is proved. The working process of RHC controller is presented in detail, and the stability of close formation controller is also analyzed. Finally, three simulation experiments are performed, and the simulation results show the feasibility and validity of our proposed control algorithm.

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“…Utilizing real-time optimization, RHC is a very effective online predictive control strategy [35]- [37]. The receding horizon is a sliding time frame, within which the original problem is solved as several smaller problems for a reduced computational burden.…”

Section: B Rhcmentioning

confidence: 99%

An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem

Zhan

Zhang

et al. 2010

IEEE Trans. Intell. Transport. Syst.

137

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The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficiency.

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Multiairport Capacity Management: Genetic Algorithm With Receding Horizon

Cited by 33 publications

References 16 publications

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Receding horizon control for multi-UAVs close formation control based on differential evolution

An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem

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