Metodología para la optimización y análisis de la respuesta de medios e infraestructuras en el sistema aeroportuario en operaciones invernales mediante modelos basados en algorítmos genéticos
Bibliographic record
Abstract
En este estudio, englobado dentro del campo de la investigación operacional en aeropuertos, se considera el problema de la optimización de la secuencia de descontaminación de nieve de los tramos que componen el área de maniobras de un aeropuerto, denominado RM-AM. Este problema se enfrenta a la optimización de recursos limitados para retirar la nieve de las calles de rodadura y pistas, dejándolas en un estado aceptable para la operación de aeronaves. El campo de vuelos se divide en subconjuntos de tramos significativos para la operación y se establecen tiempos objetivo de apertura al tráfico de aeronaves. Se desarrollan varios algoritmos matemáticos en los que se proponen distintas funciones objetivo, como son la hora de finalización del proceso, la suma de las horas de finalización de cada tramo, o el retraso entre la hora estimada y la hora de finalización. Durante este proceso, se van introduciendo restricciones operativas relativas al cumplimiento de objetivos operativos parciales aplicados a las zonas de especial interés, o relativas a la operación de los equipos de descontaminación. El problema se resuelve mediante optimización basada en programación lineal. Los resultados de las pruebas computacionales se hacen sobre cinco modelos de área de maniobras en los que va creciendo la complejidad y el tamaño. Se comparan las prestaciones de los distintos algoritmos. Una vez definido el modelo matemático para la optiamización, se propone una metodología estructurada para abordar dicho problema para cualquier área de manobras. Se define una estrategia en la operación. Se acomete el área de maniobras por zonas, con la condición de que los subconjuntos de tramos significativos queden englobados dentro de una sola de estas zonas. El problema se resuelve mediante un proceso iterativo de optimización aplicado sucesivamente a las zonas que componen el área de maniobras durante cada iteración. Se analiza la repercusión de los resultados en los procesos DMAN, AMAN y TP, para la integración de los resultados en el cálculo de TSAT y EBIT. El método se particulariza para el caso del área de maniobras del Aeropuerto Adolfo Suárez Madrid Barajas. ABSTRACT This study, which lies within the field of operations research in airports, considers the optimisation of the sequence for clearing snow from stretches of the manoeuvring area of an airport, known as RM-AM. This issue involves the optimisation of limited resources to remove snow from taxiways and runways thereby leaving them in an acceptable condition for operating aircraft. The airfield is divided into subsets of significant stretches for the purpose of operations and target times are established during which these are open to aircraft traffic. The study contains several mathematical models each with different functions, such as the end time of the process, the sum of the end times of each stretch, and gap between the estimated and the real end times. During this process, we introduce different operating restrictions on partial fulfilment of the operational targets as applied to zones of special interest, or relating to the operation of the snow-clearing machines. The problem is solved by optimisation based on linear programming. Computational tests are carried out on five distinct models of the manoeuvring area, which cover increasingly complex situations and larger areas. The different algorithms are then compared to one other. Having defined the mathematical model for the optimisation, we then set out a structured methodology to deal with any type of manoeuvring area. In other words, we define an operational strategy. The airfield is divided into subsets of significant stretches for the purpose of operations and target times are set at which these are to be open to aircraft traffic. The manoeuvring area is also divided into zones, with the condition that the subsets of significant stretches lie within just one of these zones. The problem is solved by an iterative optimisation process based on linear programming applied successively to the zones that make up the manoeuvring area during each iteration. The impact of the results on DMAN, AMAN and TP processes is analysed for their integration into the calculation of TSAT and EBIT. The method is particularized for the case of the manoeuvring area of Adolfo Suarez Madrid - Barajas Airport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".