Abstract
Airline seat inventory optimization is a very profitable tool for airline. Current researches are focused on network seat inventory optimization, which has high complication of combination of the ODF (Origin, Destination, Fare) and seat number. Due to the large number of decision variables, traditional optimization models are hard to compute. Although some LP approximation methods of traditional models improve their practical applicability, they still take long time to compute and have high complexity when network is large. We used ant colony algorithm to solve network seat inventory optimization in this paper. It is shown that ant colony algorithm can solve problem quickly and gain good results, and it is easy to implement.
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