A sustainable optimization framework for glacier tourism based on entropy-genetic algorithm

Abstract

This research addresses sustainable tourism management challenges in Juneau, Alaska, focusing on the Mendenhall Glacier tourism system affected by climate change and overtourism. We develop a comprehensive modeling framework integrating economic efficiency, environmental preservation, and social harmony through the Entropy Weight Method (EWM) and Rank Sum Ratio (RSR) evaluation model. The framework employs a genetic algorithm-based optimization approach to determine optimal tourist capacity and resource allocation, while incorporating dynamic feedback mechanisms for tourist flow management and tax revenue distribution. Model validation through comparison with Qingdao, China, demonstrates its effectiveness in generating location-specific solutions. The results recommend an optimal annual tourist capacity of 1.5 million visitors for Juneau, with tax revenue distribution of 42.1% for environmental protection, 31.6% for infrastructure development, and 26.3% for community programs. This research provides a quantitative, adaptable framework for balancing tourism development with environmental conservation and community well-being, offering practical solutions for destinations facing similar challenges.