Study On The Design Of Urban Multi-day Tour Route With Hotel Selection

In recent years,urban tourism has become one of the most popular tourism modes at worldwide,and market demand has continued to rise,becoming an important driving force for urban economic and social development.At the same time,with the advent of the post-modern tourism and experience economy era,tourists take the pursuit of personalized experience as their main goal in tourism activities.Therefore,urban tourism destinations are constantly exploring how to meet the personalized experience needs of tourists,thereby enhancing the attractiveness of the city as well as its competitive advantage.With the development of digital technologies such as mobile Internet,big data,and artificial intelligence,the tourism industry has gradually shifted towards modernization and intelligence.It is practical and necessary to provide customized services for tourists with the help of intelligent service systems.Therefore,the design of tourism recommendation system has become a popular research field,and its core problem is to explore the method of designing personalized tour routes based on intelligent algorithms.In the current research on urban tourist trip design,most of the research objects are day tour route design based on time constraint.Although there are some research of multi-day tourist route design considering hotel selection,there are still several problems in the following aspects.One is to treat the hotel as a non-profit vertex,ignoring the importance of the accommodation experience;the second is to treat the length of stay as a fixed number,ignoring that under the constraints of time and cost,the dynamic design process of the tourist route will influence the travel days;the third is that only a certain type of hotel is considered,ignoring the personalized preference of tourists when selecting hotels.Due to the small data base,only smallscale optimization problem was finally solved.Based on the above gaps,this research regards hotels as profitable vertices,considers all available hotels in the city,and sets the number of travel days as a decision variable.Under the dual constraints of time budget and cost budget,two objectives,namely,the total utility of attractions and the average utility of accommodation,have been constructed in the mathematical model,so as to design personalized multi-day tour route for urban tourists.In order to solve the model,this paper designs a hybrid heuristic algorithm.It has three improvement advantages.One is to use variable length sequence to encode the route,so that the solution result is dynamically changed in the number of travel days;the other is that a cluster-based greedy algorithm is used to construct the initial route set,which has outstanding advantages in the quality,diversity as well as computational efficiency;the third is to adequately solve the problem with discrete and continuous variables by integrating discrete particle swarm optimization algorithm,differential evolution algorithm and local search strategy.The improved discrete particle swarm optimization algorithm shows good fast convergence in multi-objective optimization problem,and the local search strategy can further optimize the quality of the solution.In order to test the performance of the designed algorithm,this research takes a case study in Chengdu.The Wilcoxon test results of paired samples show that the performance of the algorithm is significantly better than other methods which are widely used in multi-objective optimization problems,and the clusterbased greedy method and local search strategy both enhance the overall performance of the algorithm.Therefore,from a theoretical perspective,this research has enriched the multi-day tour route design research in terms of models and algorithms,and solved large-scale optimization problem.From a practical perspective,the multi-objective optimization results obtained based on Pareto theory can provide tourists with more diversified and flexible multiday travel plans to meet their individual needs.The research results are of great significance to the improvement of tourism recommendation system,and help urban tourism destinations attract more overnight tourists and increase tourism revenue.