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Multi-airport collaborative scheduling robust optimization method based on differential evolution algorithm

A differential evolution algorithm, robust optimization technology, applied in computing, instrumentation, data processing applications, etc., can solve problems such as little research, achieve the effect of large implementability and solve uncertainty problems

Active Publication Date: 2017-11-24
BEIHANG UNIV
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Problems solved by technology

At present, there is little research on the problem of collaborative flight scheduling among multiple airports.

Method used

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  • Multi-airport collaborative scheduling robust optimization method based on differential evolution algorithm
  • Multi-airport collaborative scheduling robust optimization method based on differential evolution algorithm
  • Multi-airport collaborative scheduling robust optimization method based on differential evolution algorithm

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Embodiment Construction

[0023] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0024] A method for robust optimization of multi-airport collaborative dispatch based on differential evolution algorithm provided by the present invention, specifically includes the following steps:

[0025] The first step is to establish an airport delay model.

[0026] Such as figure 1 As shown in the figure,

[0027]

[0028] Delays until the plane takes off are attributed to the departure airport, at which point the delay is attributed to the arrival airport. Delays incurred in the air are allocated to the arrival airport. If you take off early, the delay at the departure airport is 0. Initial delays are delays at the airport due to inherent issues such as mechanical issues, local adverse weather conditions, increased demand, etc. It contains the EDCT expected departure time of release, but the invention separates the initial delay from t...

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Abstract

The invention discloses a multi-airport collaborative scheduling robust optimization method based on a differential evolution algorithm, and belongs to the technical field of optimization design. The method comprises the steps of establishing an airport delay model, establishing a robust optimization model and robust optimization design. The ranking of the first four airports of the highest propagation delay is calculated according to the historical data. Supposing that the four airports form a closed network, the flight sequencing problem between the four airports is adjusted, a multi-airport collaborative scheduling robust optimization model is established with the minimum total amount of delay acting as the target, and the differential evolution algorithm is designed to solve the multi-airport collaborative scheduling robust optimization model. The model has high feasibility so that the uncertainty problem in multi-airport collaborative scheduling can be solved.

Description

technical field [0001] The invention belongs to the technical field of optimization design, and in particular relates to a robust optimization method for multi-airport coordinated scheduling based on a differential evolution algorithm. Background technique [0002] Departure scheduling of flights in the departure terminal area is one of the core issues in air traffic flow management. It aims to provide a reasonable and effective scheduling plan for the flight to be taken off, and achieve the purpose of shortening the flight waiting time and reducing the delay under the premise of ensuring safety, and has significant social and economic benefits. In fact, flight departure scheduling is a dynamic process with uncertain factors; therefore, it is necessary to study robust flight departure scheduling methods in terminal areas that are independent of specific scenarios. Robust optimization is based on the consideration of worst-case optimization, that is, the solution still maint...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/30
CPCG06Q10/04G06Q10/0631G06Q10/067G06Q50/40
Inventor 曹先彬杜文博安海超高旭鑫李宇萌
Owner BEIHANG UNIV
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