Supporting scheduling decisions by using genetic algorithm based on tourists’ preferences

Abstract

As the tourism industry switches from mass to personalized solutions, tourists’ preferences should be considered. This study aims to optimize tourists’ activity chains based on their preferences and 10 realistic parameters. A method using activity-based modeling and genetic algorithm is developed. The method handles flexible touristic activities with time window restrictions. Then scenarios including private vehicles (PV) and public transport (PT) are created. The results show high priority for security and provide significant reduction in journey times, particularly in case of flexible activities. The outcomes confirm that the proposed method provides an optimized solution to generate personalized travel itineraries that enhance tourists’ travel experience. To evaluate the performance of the algorithm, Wilcoxon Signed-Ranks Test is applied. The statistical results confirm that the performance of the GA significantly differs in the various scenarios. Travelers can benefit from the proposed approach since it offers a solution to complex real-world scheduling problems. In addition, travel patterns can be derived thus supporting the work of planners and decision-makers in the tourism industry.