This paper presents a study on optimizing truck platooning for a road-network capacitated vehicle routing problem with time windows (RNCVRPTW). The research focuses on minimizing total operation costs, including vehicle dispatch and energy costs, while fulfilling all delivery demands within their time windows. The study introduces a 3-stage algorithm that integrates a "route-then-schedule" scheme, dynamic programming, and modified insertion heuristic to solve the problem efficiently. The algorithm is tested through numerical experiments to validate its effectiveness and quantify the benefits of truck platooning technology. Truck platooning, a technology that allows multiple trucks to travel together on highways, offers significant benefits such as energy savings, reduced operational costs, and improved driver safety. However, existing studies on truck platooning typically focus on scenarios where each truck serves only one customer, limiting the application to specific cases. In contrast, this study addresses the more complex scenario where each truck may serve multiple customers located at different places, requiring the determination of both the routing and time schedules for each truck, as well as the allocation of customers to each truck and their visiting sequence. The study proposes a mixed integer programming (MIP) framework to model the problem, which is then solved using a 3-stage algorithm. The algorithm first groups customer nodes based on time-window feasibility, then constructs routes for each truck using a modified insertion heuristic, and finally schedules the trucks to optimize platooning benefits. The algorithm is tested on a toy example to demonstrate the impact of truck platooning on delivery costs, showing that platooning can lead to significant energy savings. The study also introduces a novel iterative solution algorithm that combines the "route-then-schedule" scheme, dynamic programming, and modified insertion heuristic. This algorithm is designed to efficiently solve large-scale problems and is validated through numerical experiments. The results show that the proposed algorithm can effectively optimize truck platooning plans for RNCVRPTW, leading to reduced operational costs and improved efficiency in logistics operations. The study highlights the importance of considering both routing and scheduling in truck platooning and demonstrates the potential of platooning technology to enhance the efficiency and sustainability of logistics systems.This paper presents a study on optimizing truck platooning for a road-network capacitated vehicle routing problem with time windows (RNCVRPTW). The research focuses on minimizing total operation costs, including vehicle dispatch and energy costs, while fulfilling all delivery demands within their time windows. The study introduces a 3-stage algorithm that integrates a "route-then-schedule" scheme, dynamic programming, and modified insertion heuristic to solve the problem efficiently. The algorithm is tested through numerical experiments to validate its effectiveness and quantify the benefits of truck platooning technology. Truck platooning, a technology that allows multiple trucks to travel together on highways, offers significant benefits such as energy savings, reduced operational costs, and improved driver safety. However, existing studies on truck platooning typically focus on scenarios where each truck serves only one customer, limiting the application to specific cases. In contrast, this study addresses the more complex scenario where each truck may serve multiple customers located at different places, requiring the determination of both the routing and time schedules for each truck, as well as the allocation of customers to each truck and their visiting sequence. The study proposes a mixed integer programming (MIP) framework to model the problem, which is then solved using a 3-stage algorithm. The algorithm first groups customer nodes based on time-window feasibility, then constructs routes for each truck using a modified insertion heuristic, and finally schedules the trucks to optimize platooning benefits. The algorithm is tested on a toy example to demonstrate the impact of truck platooning on delivery costs, showing that platooning can lead to significant energy savings. The study also introduces a novel iterative solution algorithm that combines the "route-then-schedule" scheme, dynamic programming, and modified insertion heuristic. This algorithm is designed to efficiently solve large-scale problems and is validated through numerical experiments. The results show that the proposed algorithm can effectively optimize truck platooning plans for RNCVRPTW, leading to reduced operational costs and improved efficiency in logistics operations. The study highlights the importance of considering both routing and scheduling in truck platooning and demonstrates the potential of platooning technology to enhance the efficiency and sustainability of logistics systems.