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Innovations in Computational Logistics and Supply Chain Analytics
Theories, Methods, and Applications
Overview
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.
Applications
The book describes new applications of machine learning and data analytic techniques to solve transport, logistic, and supply chain optimization problems. It 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.
Importance
The authors also highlight the importance of embedding and using computational methods to improve transport, logistic, and supply chain processes.
Case Studies
The authors introduce case studies of logistic and supply chain processes improved using innovative learning-based or data-driven methods.
