Otimização Impulsionada por IA para Entrega de Última Milha Sustentável

General Objectives: 1. Develop optimization solutions for the Vehicle Routing Problem (VRP) with a focus on last-mile delivery, utilizing advanced Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance the efficiency and sustainability of existing logistic systems. 2. Improve the sustainability of delivery operations, by minimizing environmental impact through route optimization that considers ecological factors, in addition to traffic and geographical conditions. 3. Integrate real-time data analysis into the routing decision process, allowing for dynamic adjustments to changes in traffic, weather conditions, and other variable factors. Specific Objectives: 1. Extract and analyze extensive features from zones within the available delivery data, identifying key geographical and socio-economic characteristics that impact delivery efficiency. 2. Develop advanced AI models, leveraging Deep Learning and Transformer algorithms to overcome the limitations of traditional route optimization methods and adapt to real-world logistics challenges. 3. Evaluate the effectiveness and adaptability of the developed AI models in real-world scenarios, ensuring that the solutions are practical, scalable, and capable of delivering tangible improvements in delivery times, sustainability, and overall efficiency. 4. Bridge the gap between theoretical route optimization models and practical delivery challenges, by incorporating insights from experienced drivers and real-time data into AI-driven routing decisions. 5. Contribute to the creation of greener and more socially responsible logistic solutions, by emphasizing sustainability and environmental considerations in the optimization of last-mile delivery routes.