Optimization Decision for Medical Waste Reverse Logistics Location Based on Genetic Algorithm

This research focuses on the site selection problem of medical waste recovery points, considering its unique characteristics, and constructs an objective function incorporating multiple elements such as recovery point coverage radius, medical waste capacity constraints, and transportation distances. To efficiently address this complex site selection problem, we employed a genetic algorithm-based site selection model, combined with improved solution strategies to solve the model, significantly enhancing the algorithm's efficiency and quality. In practical implementation, we integrated the powerful mathematical simulation programming tool, Matlab R2021a, with our genetic algorithm model to solve specific case studies. The experimental results validated the effectiveness and practicality of the genetic algorithm model in addressing medical waste recovery point site selection problems, providing a robust theoretical foundation for related decision-makers. This study not only showcases the significant role of computer science in solving complex problems but also offers new insights and methodologies for algorithm optimization and application. It underscores the importance of computational approaches in contributing to better decision-making, particularly in intricate logistics network optimization challenges.