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Effective waste management is essential for maintaining a clean and sustainable urban environment. This paper addresses the waste collection challenges in Torreón, Coahuila, Mexico, by integrating data mining techniques with the vehicle routing problem (VRP). The goal is to optimize waste collection routes and schedules to minimize costs, reduce fuel consumption, and enhance operational efficiency. The study leverages historical waste collection data, demographic information, and geographic data to comprehensively understand waste generation patterns, population density, and infrastructure layout. The data mining process involves exploratory analysis, clustering, and classification techniques to identify critical variables and patterns that influence waste generation and collection. This information is used to design efficient collection routes, considering factors such as distance, capacity constraints, traffic patterns, and time windows. The VRP model is then applied to allocate waste collection vehicles optimally, ensuring that each route is serviced within the designated timeframe. The model aims to minimize the number of vehicles used, optimize capacity utilization, and reduce travel distances. Additionally, real-time data on vehicle status, traffic conditions, and waste generation rates are integrated to adjust routes and schedules to improve operational efficiency dynamically. This approach provides waste management authorities in Torreón with valuable insights and tools to enhance waste collection operations. By leveraging data mining and VRP techniques, the study aims to improve waste management practices, reduce environmental impact, and promote a cleaner, healthier environment for residents of Torreón, Coahuila, Mexico. |
