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Full Description
This book investigates the role of optimization methods and data science techniques in improving both internal and external logistics operations, with particular emphasis on last-mile logistics. It explores mathematical models, heuristics, and data-driven approaches for enhancing routing, inventory management, resource allocation, and operational planning under real-world constraints. Special attention is given to the civil applications of emerging technologies, including artificial intelligence, IoT, AGV and UAV, as key enablers of more efficient and sustainable logistics processes. The book also examines the environmental and organizational challenges facing contemporary logistics, with the objective of reducing emissions, congestion, energy consumption, and operational inefficiencies across both urban and rural contexts. By integrating methodological perspectives with practical case studies, it provides a rigorous framework for the design of smarter and more sustainable systems.
Contents
Part I. Sustainable Approaches to Urban Freight and City Planning.- Chapter 1. Integrating ADRs with Public Transit for Sustainable Logistics: A Case Study in Naples.- Chapter 2. The Vehicle Routing Problem with Autonomous Delivery Robots and Public Transit.- Chapter 3. Optimizing Parcel Locker Networks: Case Studies in Naples and Catani.- Chapter 4. Urban Green Area Planning in Multi-City Environments.- Part II. Optimizing AGV and UAV Systems for Civil Use.- Chapter 5. Truck-Drone Delivery in Practice: A Local Search Metaheuristic with a Real-World Pharmaceutical Case Study.- Chapter 6. A close-enough event-driven approach for the dynamic assignment of targets to a fleet of autonomous vehicles.- Chapter 7. A MILP-based Heuristic Approach for the Multiple Close-Enough Traveling Salesman Problem.- Part III. Automated and Sustainable Warehouses.- Chapter 8. A dynamic programming algorithm for an order picking problem with deadlines.- Chapter 9. A mixed-integer linear program and a carousel greedy algorithm for the scheduling of pick-up and delivery operations in automated warehouses.- Chapter 10. Low-Carbon Warehouse Scheduling via GRASP Optimization.- Part IV. Leveraging Data and Optimization for Added Value.- Chapter 11. A Lagrangean-Based Cut for a Branch-and-Cut Approach to the Resource-Constrained Covering Tour Problem.- Chapter 12. Semisupervised classification for active learning.
