SIMMOD Based Simulation Optimization of Flight Delay Cost for Multi-airport System

2008 ◽  
Author(s):  
Wei Gao ◽  
Xiaohao Xu ◽  
Lin Diao ◽  
Hongjun Ding
Keyword(s):  
Simulation Optimization ◽  
Delay Cost ◽  
Flight Delay

Stochastic Simulation Optimization for Route Selection Strategy Based on Flight Delay Cost

2018 ◽  
Vol 35 (06) ◽  
pp. 1850045
Author(s):  
Yong Tian ◽  
Bojia Ye ◽  
Marc Sáez Estupiñá ◽  
Lili Wan
Keyword(s):  
Stochastic Simulation ◽  
Simulation Optimization ◽  
Optimization Technique ◽  
Civil Aviation ◽  
Selection Strategy ◽  
Route Selection ◽  
Full Information ◽  
Adverse Weather ◽  
Delay Cost ◽  
Flight Delay

The continuous and strong growth of the civil aviation in the world combined with the severe adverse weather problem have made necessary the collaboration between the different civil aviation agents to improve the management of the capacity-demand imbalances in the airspace. In this paper, we consider a stochastic simulation optimization problem for air route selection strategy based on flight delay cost. The problem takes consideration of airspace capacity and demand uncertainty, three strategies, including collaborative reroute strategy (CRS), full information reroute strategy (FIRS) and hybrid stated route preference strategy (HSR), are employed to mitigate the flight delay cost. To find the best strategy, a discrete event simulation model is built by Arena Software, and the Monte Carlo method combined with the OCBA simulation optimization technique is employed for assessing a common severe convective weather scenario in the Central and Southern China airspace. Simulation results imply that HSR schemes show better system-wide performance than CRS and FIRS, these benefits are supposed to come from the batch allocations method. Although the airline can receive full information in advance, FIRS does not show obvious advantage in reducing the total airborne waiting time than CRS. For the system-wide performance FIRS is better than CRS, but not as good as HSR.

scholarly journals A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Jiang ◽  
Zhaolong Xu ◽  
Xinxing Xu ◽  
Zhihua Liao ◽  
Yuxiao Luo
Keyword(s):  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Objective Functions ◽  
Total Delay ◽  
Schedule Optimization ◽  
Delay Cost ◽  
Flight Delay

In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair deviation are decreased by 42.22% and 38.64%, respectively. Therefore, the optimization model makes great significance in reducing flight delay and improving the fairness among all airlines.

Simulation Optimization: A Review on Theory and Applications

2013 ◽  
Vol 39 (11) ◽  
pp. 1957 ◽  
Author(s):  
Long-Fei WANG ◽  
Le-Yuan SHI
Keyword(s):  
Simulation Optimization

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

Aircraft Assembly Ramp-Up Planning Using a Hybrid Simulation-Optimization Approach

2020 ◽  
Author(s):  
Amos H.C. Ng ◽  
Jacob Bernedixen ◽  
Martin Andersson ◽  
Sunith Bandaru ◽  
Thomas Lezama
Keyword(s):  
Simulation Optimization ◽  
Hybrid Simulation ◽  
Optimization Approach ◽  
Aircraft Assembly
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