Phd in INFRASTRUCTURES AND TRANSPORT

Thesis title: Green Vehicle Routing and Logistics Optimization: IoT-Driven Solutions for Modern Urban Challenges

This thesis tackles the evolving challenges of urban logistics through the development of advanced optimization models and algorithms aimed at enhancing the efficiency, sustainability, and flexibility of logistics networks. With the rapid pace of urbanization, cities are increasingly confronted with complex issues related to waste management, cold chain logistics, and the integration of electric vehicles (EVs). Traditional vehicle routing models are inadequate in addressing dynamic customer demands, environmental regulations, and the growing influence of technologies such as the Internet of Things (IoT) and Industry 4.0. The research presents a series of interrelated studies designed to improve vehicle routing and logistics optimization, addressing both operational and environmental challenges. Key focus areas include waste collection, cold chain logistics, and EV routing—each requiring innovative solutions due to real-world complexities such as limited vehicle range, recharging infrastructure, dynamic traffic conditions, and customer-specific requirements. Chapter Highlights • IoT-Enhanced Waste Collection: The first study introduces IoT-based monitoring systems to optimize waste collection routes, leveraging heuristic and metaheuristic algorithms to reduce costs and improve efficiency. • Two-Echelon Waste Management: The second study enhances waste collection by implementing a two-echelon distribution system that synchronizes temporal and spatial operations to streamline logistics. • Cold Chain Logistics: The third study focuses on cold chain logistics, optimizing vehicle routing and facility locations to manage temperature-sensitive goods and varying customer demands. • Electric Vehicle Routing in Dynamic Networks: The fourth study incorporates real-time traffic data to optimize electric vehicle routing in dynamic urban environments, considering both travel times and energy consumption. • Two-Echelon EV Routing with Battery Swap Stations: The fifth study explores a two-echelon logistics system for EVs, integrating battery swapping stations to overcome range limitations and enhance delivery efficiency. This thesis demonstrates how the integration of IoT, Industry 4.0, and EV technologies can optimize urban logistics, significantly reducing both operational costs and environmental impacts. It advances the field by transitioning from single-echelon to multi-echelon systems, incorporating dynamic models, and developing scalable, real-world solutions. The findings are applicable across various logistics sectors, offering a pathway toward more sustainable urban transportation and supply chain networks. Keywords: Multi-Echelon Distribution Systems; Green Vehicle Routing; Vehicle Routing Problem; Heuristics and Metaheuristic Algorithms; Reverse Logistics; IoT-Based Optimization, Multi-Objective Optimization.