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Generative Adversarial Networks (GANs) and Optimization in the Airline Industry
Abstract
This article examines the potential of Generative Adversarial Networks (GANs) in optimizing airline operations. GANs leverage adversarial training to generate high-quality synthetic data, addressing challenges in scheduling, demand forecasting, route planning, disruption handling, pilot training, and passenger segmentation. Applications include stress-testing scheduling algorithms, simulating disruption scenarios to enhance resilience, and augmenting passenger data for personalized marketing. A mathematical formulation for hub-and-spoke disruption handling highlights GANs' role in refining optimization inputs and cost predictions. Practical examples demonstrate how GANs augment traditional models, improve efficiencies, and future-proof operations. The chapter concludes with insights into integrating GANs with emerging technologies to drive innovation in aviation.
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