Site Selection for Public Container Return Points Based on Adaptive Genetic Algorithm

To enhance the accuracy of the site selection for public container return points, this paper integrates the specific characteristics of large logistics parks, constructs an objective function that comprehensively considers multiple cost factors including fixed investment costs, management costs, and variable costs. By incorporating various coefficients such as multimodal transportation accessibility and land prices into the function, it aligns more closely with real-world scenarios, thereby establishing a site selection model for public container return points. An optimized adaptive genetic algorithm is employed to improve the model’s performance in terms of solution efficiency and quality. Utilizing Matlab2022a for empirical analysis of the algorithmic model, the results of this study validate the effectiveness and practical application value of the genetic algorithm model in addressing the site selection issue for public container return points.