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.