Publication of the Second Edition of the Supply Chain and Logistics Book

The College of Business extends its congratulations to the Editorial team on the successful publication of the second edition of its focus-area book series in Supply Chain and Logistics, in collaboration with the international publisher Springer.

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.

Table of contents (8 chapters)

1. Multi-Capital Sustainability Assessment in Agro-Food Supply Chains: A Systematic Review of Indicators and Stakeholder Perspectives
2. A Review of SHAP and LIME for Enhancing Classification Transparency in Real-Life Logistics and Supply Chain Management Problems
3. Sustainable Urban Transportation Through Optimized Charging Station Placement with Geographic and Multi-Criteria Analysis
4. Operationalizing Auction-Based Strategic Carriers’ Selection in Distribution Networks
5. Optimizing Home Health Care Decisions: A Bi-level Evolutionary Approach Considering Patient Preferences
6. An Evolutionary Algorithm-Based Decision Support System for Bi-objective Hazardous Materials Transportation
7. Optimal Green Investment Timing Under Linear Progressive Carbon Tax Legislation
8. Case-Based Reasoning for Solving Container Pre-Marshalling Problem in Seaport Terminals

المزيد