Two-stage stochastic programming model for capacitated complete star p-hub network with different fare classes of customers

In this paper, a stochastic programming approach is applied to the airline network revenue management problem. The airline network with the arc capacitated single hub location problem based on complete–star p-hub network is considered. We try to maximize the profit of the transportation company by choosing the best hub locations and network topology, applying revenue management techniques to allocate limited perishable capacity and provide booking limits for all itineraries and fare classes. In order to characterize the uncertainty of demand in the airline market, we introduce stochastic variations caused by seasonally passengers’ demands through a number of scenarios. The proposed model deals with finding the location of hub facilities, the assignment of demand nodes to these located hub facilities and allocating the limited capacity of aircraft seats on each rout to different customer classes in order to maximize the profit. Due to the computational complexity of the resulted model, a hybrid algorithm improved by a caching technique based on standard genetic operators is used to find a near optimal solution of the problem. Numerical experiments are carried out on the Turkish network data set. The performance of the solutions obtained by the proposed algorithm is compared with the pure GA and Imperialist Competitive Algorithm in terms of the computational time requirements and solution quality.