Abstract
To assist cruise companies in designing the technic-economic specifications of “fly-cruise” products and attracting international tourists, this paper proposes a model of itinerary planning and dynamic pricing with advanced algorithm. We integrate the Reinforcement Learning (RL) and Genetic Algorithm (GA) to develop a hybrid algorithm to obtain the optimal solution of the model. Taking Malaysian consumers travelling to China for cruise tours as a case study, we empirically test the model with real market data and use it to analyze the impact of innovating “fly-cruise” products on the deficit of China's inbound and outbound tourists. The results show that the innovating “fly-cruise” products will attract 20.52 million Southeast Asian tourists to China annually and generate a profit of US$ 3.56 billion for cruise companies. It highlights that the advanced algorithm based “fly-cruise” products can help China to effectively reduce the deficit of inbound and outbound tourists and improve the international balance of service trade.
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