Research on Tourism Route Optimization Based on Entropy-Weighted TOPSIS and Greedy Algorithm

This study aims to optimize a tourism route based on the Entropy-Weighted TOPSIS and Greedy Algorithm. Initially, we conducted data preprocessing and collection, cleaning and normalizing multi-dimensional indicator data for 352 cities nationwide to ensure the accuracy and reliability of the analysis results. Subsequently, we employed an integrated evaluation model combining Principal Component Analysis (PCA) and the Entropy Weight method to assess city attractiveness, identifying the top 50 cities with the highest international tourism appeal. In the PCA steps, we standardized the data, calculated the covariance matrix, performed eigenvalue decomposition, selected principal components, and determined the number of principal components based on the cumulative variance contribution rate. The Entropy Weighted TOPSIS evaluation model assessed city tourism attractiveness by standardizing data, calculating entropy values, determining weights, solving for ideal and negative ideal solutions, calculating distances, and TOPSIS scores. Finally, we developed an optimized tourism route planning model based on the high- speed rail network, considering key factors such as total travel time, duration of stay at each attraction, and admission fees. Using a Greedy Algorithm strategy, we determined the best route to maximize access to high-quality attractions within time constraints. The model's solution includes initializing parameters, finding the optimal next city, calculating high-speed rail travel time and costs, determining the travel time and ticket prices of attractions, calculating the total travel time, and updating the current status. Ultimately, we planned the optimal travel route through the model.