Tourism Sustainability Optimization in Juneau: Integrated Application of Genetic Algorithm and Analytic Hierarchy Process

With the rapid development of tourism in Juneau City, while bringing considerable income to the city, it also brings problems of congestion and retreating glaciers. In order to manage these hidden costs brought by tourism and establish a sustainable tourism industry, this paper based on genetic algorithm, established an optimization model to study the tourism industry. Aiming at the first problem, firstly, a travelable model based on genetic algorithm is established, which determines the objective function by maximizing the total utility. Then, we use curve regression to establish the relationship between social cost and cost and the number of tourists, respectively. In addition, considering the relationship between various factors and existing data, we establish the ecological index and survival index SQ, and assign weights to various indicators by entropy method. Then we use ARIMA to predict the SQ and Env in 2024 based on the data from 2013 to 2023.Then, according to the principle of 3σ, we obtain the constraint condition that Env≥〖Env〗_min=0.197 and SQ≥〖SQ〗_min=0.321. Considering the tourist situation in Juneau, we also use the same limit on the number of tourists that P≤1187782. Finally, the optimal solution is u = 10.886977 (see Table 7). Afterwards, according to the requirements of the topic, we conduct the model, and the results show that the impact of per capita consumption expenditure y on u is relatively limited, and the model is robust. Finally, we use the Analytic Hierarchy Process to compare eleven indicators, and the results show that Standard deviation of glacier area, Gross total (carbon emission), Median Earnings Growth, Unemployment Rate and other indicators have a great impact on the final decision (total income), and they will be used as key factors in the later stage. Aiming at the second problem, this paper applies the tours model based on genetic algorithm to the ancient city, and extends to the scenic spots with few tourists, such as Zhangye Danxia Geopark. First of all, for the ancient city, we collected and processed relevant data, analyzed parameters such as the average consumption expenditure of tourists, and then used genetic algorithm to optimize, and obtained the optimal strategy of the ancient city and the corresponding optimal value as shown in Table 9. Then, we extend the model to Zhangye Danxia Geopark. According to the characteristics of the small number of tourists, appropriate constraints such as ecological index, survival index and number of tourists are used to maximize the total utility. Finally, we get the tourism strategy which is suitable for Zhangye Danxia Geopark. In response to Question 3, we wrote a memorandum for the Juno City Tourism Committee. Through ecological indicators and survival indexes, we proposed that tourism can be realized from the aspects of tourist management, strengthening infrastructure construction, scenic spots and protection, ecology and protection, and policies and regulations. Our model can not only provide scientifically effective policy support for Juneau City but also be generalized to other cities affected by tourism.