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Article summary:

1. Airline crew planning is a complex process that is divided into two phases: crew pairing and crew rostering.

2. Airlines are increasingly adopting a dynamic crew request approach, in which feedback is given to the crew member shortly after the request is expressed.

3. This paper presents a neural network classification algorithm to assess each flight request, based on features from the request, flight and current status of the schedule.

Article analysis:

The article provides an overview of the challenges associated with dynamic evaluation of airline Crew’s flight requests using a neural network. The article does not provide any evidence for its claims or any counterarguments to its proposed solution. It also does not explore any potential risks associated with this approach or present both sides equally. Furthermore, there is no discussion of potential biases or sources of bias in the data used to train and test the neural network model. Additionally, there is no mention of how reliable or accurate the model will be in practice, nor any discussion of how it might be improved upon in future iterations. Finally, there is no discussion of how this approach might affect other aspects of airline operations such as customer satisfaction or operational efficiency. In conclusion, while this article provides an interesting overview of one possible solution to dynamic evaluation of airline Crew’s flight requests using a neural network, it lacks sufficient evidence and exploration into potential risks and biases associated with this approach.