Ant Colony Algorithm and Simulation for Robust Airport Gate Assignment

Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved

Real-Time Gate Reassignment Based on Flight Delay Feature in Hub Airport

Appropriate gate reassignment is crucially important in efficiency improvement on airport sources and service quality of travelers. The paper divides delay flight into certain delay time flight and uncertain delay time flight based on flight delay feature. The main objective functions of model are to minimize the disturbance led by gate reassignment in the case of certain delay time flight and uncertain delay time flight, respectively. Another objective function of model is to build penalty function when the gate reassignment of certain delay time flight influences uncertain delay time flight. Ant colony algorithm (ACO) is presented to simulate and verify the effectiveness of the model. The comparison between simulation result and artificial assignment shows that the result coming from ACO is obvious prior to the result coming from artificial assignment. The maximum disturbance of gate assignment is decreased by 13.64%, and the operation time of ACO is 118 s. The results show that the strategy of gate reassignment is feasible and effective

Modelo para identificar los vuelos afectados por retrasos o cancelaciones en el aeropuerto El Dorado de Bogotá, Colombia

Este trabajo está basado en el análisis de factores climáticos y operacionales de las aerolíneas con operación en Colombia. El factor operacional contiene el detalle de los vuelos que tienen lugar en los aeropuertos del país con variables como origen, destino, número de vuelo, aerolínea, fecha y hora programada, fecha y hora de remolque, estado del vuelo (adelantado, cumplido, retrasado y cancelado), cantidad de pasajeros, cantidad de carga, distancia y tiempo de vuelo entre otras. Por el gran peso e importancia que tiene el Aeropuerto El Dorado de Bogotá, el análisis y modelo resultado de este trabajo se centró en la operación y factores climáticos que tienen incidencia en este terminal aéreo. Por medio de técnicas como regresión logística, redes neuronales y XGboosting se logró predecir en la base de datos de pruebas cerca del 70% de los vuelos afectados por cancelaciones o retrasos en el aeropuerto de la capital colombiana.This work is based on the analysis of weather and operational factors of the airlines operating in Colombia. The operational factor contains the detail of the flights that take place in the country's airports with variables such as origin, destination, flight number, airline, scheduled date and time, towing date and time, flight status (early, on time, delayed and canceled), number of passengers, amount of cargo, distance and flight time among others. Due to the great weight and importance of the El Dorado Airport of Bogotá, the analysis and model resulting from this work focused on the operation and weather factors that have an impact on this Airport. Using techniques such as logistic regression, neural networks and XGboosting, it was possible to predict in the test database about 70% of flights affected by cancellations or delays at the Colombian capital airport.Magíster en Analítica para la Inteligencia de NegociosMaestrí

A computational intelligence based prediction model for flight departure delays

Abstract : Flight departure delays are a major problem at OR Tambo International airport (ORTIA). There is a high delay for flights to depart, especially at the beginning of the month and at the end of the month. The increasing demand for flights departing at ORTIA often leads to a negative effect on business deals, individuals’ health, job opportunities and tourists. When flights are delayed departing, travellers are notified at the airport every 30 minutes about the status of the flight and the reason the flight is delayed if it is known. This study aims to construct a flight delays prediction model using machine learning algorithms. The flight departures data were obtained from ORTIAs website timetable for departing flight schedules. The flight departure data for ORTIA to any destination (i.e. Johannesburg (JNB) Airport to Cape Town (CPT)) for South African Airways (SAA) airline was used for this study. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi-Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. A cross-validation (CV) method was used for evaluating the models. The best prediction model was selected by using a confusion matrix. The results showed that the models constructed using Decision Trees (J48) achieved the best prediction for flight departure delays at 67.144%, while Multi-layered Perceptron (MLP) obtained 67.010%, Support Vector Machine (SVM) obtained 66.249% and K-Means Clustering (K-Means) obtained 61.549%. Travellers wishing to travel from ORTIA can predict flight departure delays using this tool. This tool will allow travellers to enter variables such as month, week of month, day of week and time of day. The entered variables will predict the flight departure status by examining target concepts such as On Time, Delayed and Cancelled. The travellers will only be able to predict flight departures status, although they will not have full knowledge of the flight departures volume. In that case, they will depend on the flight information display system (FIDS) board. This study can predict and empower travellers by providing them with a tool that can determine the punctuality of the flights departing from ORTIA.M.Com. (Information Technology Management