Last mile delivery problem
Work description
The work plan for the AI-Driven Optimization for Sustainable Last Mile Delivery (AIO-TacitR) project is structured around advancing solutions to the Vehicle Routing Problem (VRP) by leveraging Artificial Intelligence, Machine Learning, and Generative AI to improve sustainability and real-time adaptability in urban logistics. The project begins with foundational activities such as literature review, data collection, and preprocessing to build a robust knowledge base. It then advances to developing and refining AI models that integrate zone characteristics, traffic conditions, weather, and driver feedback. These models aim not only to reduce delivery times and operational costs but also to minimize environmental impacts, aligning with sustainability goals. The plan emphasizes iterative development, incorporating feedback loops from testing and environmental analysis to continuously improve performance. Over the three-year timeline, the project follows a phased approach: Year 1 focuses on groundwork—research, data preparation, and initial AI model prototypes. Year 2 emphasizes refinement, prototype testing, and detailed environmental impact assessments, while Year 3 prioritizes integration of AI systems into partner logistics operations, user experience design, validation, and large-scale dissemination of results. Cross-cutting activities like project management, stakeholder engagement, and sustainability reporting ensure the alignment of research outputs with industry needs and societal goals. The structured plan ultimately seeks to bridge the gap between theoretical optimization models and real-world delivery challenges, setting a foundation for greener, more efficient, and socially responsible logistics solutions.
Academic Qualifications
PhD in Transportation Engineering
Minimum profile required
Experience in the following skill(s):- Programming in Python, C++- Experience in last mile delivery projects- Strong foundation in machine learning, deep learning, reinforcement learning, and optimization techniques- Strong skills in data acquisition, preprocessing, and feature engineering- Background in Data Science and Data Engineering- Experience in developing multiple web applicationsFluent in Portuguese and English.
Preference factors
Capacity to lead sub-tasks, mentor junior team members (e.g., master’s students), and coordinate interdisciplinary collaboration. Strong written and verbal communication skills in English, enabling contributions to project reports, sustainability documentation, and dissemination activities. Motivation to contribute not only to technical breakthroughs but also to policy, societal, and sustainability impacts, bridging research with practice.
Application Period
Since 16 Oct 2025 to 29 Oct 2025
Centre
Industrial Engineering and Management