Abstract
Trip planning significantly improves tourists’ experiences and enhances the competitive advantage of tourist attractions. We focus on the tourist trip design problem (TTDP) under a fuzzy environment, which is an extension of TTDP that considers the spatiotemporal route structure and variable sightseeing value at points of interest. A rough approximation-based model is proposed to deal with fuzzy variables, and a hybrid genetic algorithm is designed to identify the optimal route. We conduct a numerical experiment to assess the performance of the presented approach. The results of the Wilcoxon rank sum tests indicate that our approach performs significantly better than currently available methods. The evolution strategies based on improved particle swarm optimization also demonstrate better efficiency than existing approaches.
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