Innovations in Computational Logistics and Supply Chain Analytics

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.

Introduction. - Innovative logistic techniques and Tools in Industry 4. 0. - Predictive analytics for quality control of logistic processes. - Artificial intelligence systems for smart logistics in the medicine sector. - Supply-Chain Management: Automating the purchase process based on machine learning techniques . - MultiCriteria Decision-Making applications in Supply Chain Management: Review & Practical cases. - Consumers Acceptance of Drones for last-mile delivery in emerging economy. - Evaluation methods to mitigate the risks of price fluctuations on inventory in contemporary economies. - Overview of using augmented reality techniques in logistics. - Computational methods for scheduling problems: State of the art. - Digitalization and automation of supply chain processes. - Reverse Logistics: Evaluation of practices in hospitals in Saudi Arabia. - Combining Global Optimization Algorithms with Live Google Maps for Solving the Vehicle Routing Problem. - Conclusion.