This book discusses advances in computational logistics and supply chain analytics. The book includes innovative data-driven and learning-based approaches, methods, algorithms, techniques, and tools that have been designed or applied to create and implement a successful logistics and supply chain management process. The book describes new applications of machine learning and data analytic techniques to solve transport, logistic, and supply chain optimization problems. The book gives readers an overview of innovative design and applications of computational methods issued from machine learning and data analytics domains to automate and improve transport, logistic and supply chain processes. The authors also highlight the importance of embedding and using computational methods to improve transport, logistic and supply chain processes. The authors introduce case studies of logistic and supply chain processes improved using innovative learning-based or data-driven methods.