Using a heuristic algorithm to design a personalized day tour route in a time-dependent stochastic environment

A substantial transformation has occurred in tourist behavior in the postmodern tourism era, where the tourism market is dominated by the demand for tailored experiences. Therefore, the design of personalized tourist routes plays a fundamental role in improving tourists' travel experiences and the success of tourist attractions. This study proposes a hybrid heuristic algorithm based on random simulation (RS-H2A) to design a personalized day tour route in a time-dependent stochastic environment. To evaluate the performance of this algorithm, we conducted a case study at Jiuzhai Valley in Sichuan, China. The results of paired sample t-tests indicated that the proposed algorithm performed significantly better than existing methods. Furthermore, our proposed approach had the ability to design more realistic and personalized routes for tourists than previous methods. We designed an experiment to further explore how uncertain environments affect tourists with different levels of risk awareness.