This paper addresses the optimization of truck platooning for a road-network capacitated vehicle routing problem with time windows (RNCVRPTW). Truck platooning, a technology that links trucks on highways to reduce energy consumption and operational costs, has gained significant attention. However, existing studies often focus on scenarios where each truck serves only one customer demand, leading to simplified routing and scheduling problems. This study aims to optimize the truck platooning plan for real-world logistics scenarios where each truck may serve multiple customers located at different places. The authors propose a mixed-integer programming (MIP) framework to model the operation of truck platooning in a road network, considering both dispatch costs and energy costs. They develop a 3-stage algorithm that integrates a "route-then-schedule" scheme, dynamic programming, and a modified insertion heuristic to solve the proposed model efficiently. The algorithm first determines the customers to be served by each truck, constructs routes for each truck, and assigns trucks with suitable schedules. The solution process is iterative, with each stage building on the previous one to refine the routes and schedules. The paper includes a detailed mathematical formulation of the RNCVRPTW-TP model, which accounts for the impact of truck weight on fuel consumption and the energy savings brought by platooning. The authors also present a Knapsack dynamic programming approach to group customer nodes and dispatch trucks, a modified insertion heuristic for route construction, and a scheduling approach to maximize platoon fuel savings. Numerical experiments validate the proposed modeling framework and solution algorithm, demonstrating their effectiveness in minimizing total operation costs while fulfilling all delivery demands within their time window constraints.This paper addresses the optimization of truck platooning for a road-network capacitated vehicle routing problem with time windows (RNCVRPTW). Truck platooning, a technology that links trucks on highways to reduce energy consumption and operational costs, has gained significant attention. However, existing studies often focus on scenarios where each truck serves only one customer demand, leading to simplified routing and scheduling problems. This study aims to optimize the truck platooning plan for real-world logistics scenarios where each truck may serve multiple customers located at different places. The authors propose a mixed-integer programming (MIP) framework to model the operation of truck platooning in a road network, considering both dispatch costs and energy costs. They develop a 3-stage algorithm that integrates a "route-then-schedule" scheme, dynamic programming, and a modified insertion heuristic to solve the proposed model efficiently. The algorithm first determines the customers to be served by each truck, constructs routes for each truck, and assigns trucks with suitable schedules. The solution process is iterative, with each stage building on the previous one to refine the routes and schedules. The paper includes a detailed mathematical formulation of the RNCVRPTW-TP model, which accounts for the impact of truck weight on fuel consumption and the energy savings brought by platooning. The authors also present a Knapsack dynamic programming approach to group customer nodes and dispatch trucks, a modified insertion heuristic for route construction, and a scheduling approach to maximize platoon fuel savings. Numerical experiments validate the proposed modeling framework and solution algorithm, demonstrating their effectiveness in minimizing total operation costs while fulfilling all delivery demands within their time window constraints.