The transportation industry has experienced rapid development over the past decade with the assistance of digital platforms and intelligent algorithms. The Vehicle Routing Problem (VRP) has remained a persistent challenge in transportation research. To address routing problems, experts and scholars have explored optimization models and algorithms, from the classical Traveling Salesman Problem to the more general VRP. These models and algorithms are applied in real-world industrial scenarios to achieve cost optimization and reduce carbon footprints. However, due to the complexity of real-world problems, numerous specific constraints are often added, and challenges such as information opacity, uncertainty, and irrational human behavior may arise. This paper discusses and provides solutions for three different logistic use cases involving external truck network design. Through these industrial case studies, the paper introduces how deep reinforcement learning-based vehicle routing optimization has been implemented. As a result, it can be observed that the routes optimized by the reinforcement learning agent have over 10% total cost compared to baseline results. Furthermore, the paper proposes that in future research, DRL algorithms for vehicle routing problems could be generalized into more variations of VRP. Blogger's Review: This paper demonstrates the application of deep reinforcement learning in solving the Vehicle Routing Problem, achieving notable results and showcasing its potential in real-world industrial scenarios. Further research and optimization of DRL algorithms are crucial for addressing more complex VRP variations in the future.