Abstract: In recent years,theme parks have emerged as a popular form of tourism and have gradually become a favored choice for leisure and vacation among tourists.However,how to reasonably plan itineraries to maximize the visitor experience within limited time has become an important issue in the operation and management of theme parks.In response to this phenomenon,this paper proposes a new method for designing tourist itineraries,aiming to maximize visitors’ play experience.First,based on the operational characteristics of theme parks,interest points within the park are classified,and corresponding profit functions are constructed for each category to quantify visitors’ play experiences.At the same time,considering the negative impact of waiting time and travel time on visitor experience during actual tours,these are incorporated as constraints into the model.On this basis,a mixed-integer linear programming model is constructed,with precise modeling of the time window constraints for interest points to better reflect the actual operational scenario.To effectively solve this complex optimization problem,this paper proposes an improved adaptive large neighborhood search algorithm,which significantly enhances the solution quality by dynamically adjusting the search strategy.Through extensive numerical experiments,the effectiveness and feasibility of the proposed model and algorithm are systematically verified.Finally,taking Shanghai Disneyland Resort as a case study,the proposed model and algorithm are applied to real-world scenarios,and the results show that the method can significantly improve the visitor experience,providing scientific decision-making support for theme park operations and management.

Key words: Orienteering problem, Adaptive large neighborhood search, Tourist trip design problem, Theme park