This PhD research focuses on optimizing logistics chains using advanced techniques like
combinatorial optimization and artificial intelligence. The study explores how digital
platforms can utilize combinatorial optimization to automate optimize routing and
scheduling decisions. Machine learning techniques are employed to predict demand and
enhance decision-making processes, while reinforcement learning aids in dynamically
adjusting strategies based on real-time changes. By integrating these approaches, the
research aims to streamline logistics operations, reduce costs, and improve service
reliability.
