﻿ 基于多目标规划不正常航班恢复研究 Research on Abnormal Flight Recovery Based on Multi-Objective Planning

Open Journal of Transportation Technologies
Vol. 08  No. 04 ( 2019 ), Article ID: 31485 , 6 pages
10.12677/OJTT.2019.84035

Research on Abnormal Flight Recovery Based on Multi-Objective Planning

Bing Dong, Xin Huang

Air Traffic Management College, Civil Aviation Flight University of China, Guanghan Sichuan

Received: Jul. 5th, 2019; accepted: Jul. 22th, 2019; published: Jul. 29th, 2019

ABSTRACT

The regular implementation of the airline’s flight plan can reduce the operating cost of the airline and increase passenger’s satisfaction. In actual operation, flight plans are affected by extreme weather, flow control and other factors, which will cause flights not to operate in accordance with the optimal plan, and even lead to serious flight delays. In this paper, the abnormal flight recovery of airlines was studied. Through the analysis of integrated passenger costs, crew recovery and aircraft scheduling, a multi-objective programming mathematical model was established. By selecting the two most important decision-making objectives, cost loss and total passenger delay time, different weights were assigned. The results showed that the method presented in this paper could reduce the total operating cost and passenger’s delay time.

Keywords:Air Transport, Abnormal Flights, Multi-Objective Planning, Genetic Algorithm

1. 引言

2. 不正常航班恢复基本方案

3. 不正常航班恢复问题建模

3.1. 航班恢复模型的参数分析

3.2. 模型建立

$\begin{array}{l}{t}_{i}\left(x\right)=\mathrm{min}\underset{{f}_{1}\in F}{\sum }\underset{{s}_{j}\in S}{\sum }{c}_{ij}{x}_{ij}\\ {c}_{ij}=\left({\alpha }_{i}^{yy}+{\alpha }_{i}^{yl}+{\alpha }_{i}^{lk}\right)|{s}_{j}-{d}_{i}|\end{array}$ (1)

(2)

4. 模型求解

4.1. 模型的归一化处理

$T\left(x\right)=\mathrm{min}\left\{{\lambda }_{1}{t}_{1}\left(x\right)+{\lambda }_{2}{t}_{2}\left(x\right)\right\}$ 其中： $\underset{r=1}{\overset{2}{\sum }}{\lambda }_{r}=1,r=1,2$

4.2. 基于遗传算法的求解

1) 确定种群大小n，交叉概率 ${p}_{c}$ ，变异概率 ${p}_{m}$

2) 初始化种群：产生n组可行解组成初始种群 ${p}_{0}$

3) 计算种群中个体的适应度值并进行选择操作；

4) 按照交叉概率 ${p}_{c}$ 、变异概率 ${p}_{m}$ 进行遗传操作；

5) 算法终止条件判断，如果满足终止条件，则输出最优解，算法结束，否则转步骤3。

$fitness=\frac{1}{T\left(x\right)}.$ (3)

5. 案例分析

5.1. 案例参数

Table 1. Basic information of affected flights

Table 2. Aircraft operating costs

5.2. 案例计算结果

Table 3. Loss of affected flights

Table 4. The result of reallocating slots

6. 结论

Research on Abnormal Flight Recovery Based on Multi-Objective Planning[J]. 交通技术, 2019, 08(04): 289-294. https://doi.org/10.12677/OJTT.2019.84035

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