The Dial-a-ride problem with time windows (DARPTW) is a highly complex problem with several operational applications. Compared to vehicle routing problems and pick up and delivery problems, the DARPTW considers the transport of persons and has, therefore, more constraints to ensure passengers satisfaction. These optimization problems can be modeled as a mixed integer program and be solved either to optimality or by applying heuristics. The exact solutions are costly in terms of computation time, especially in dynamic environments and for large-scale problems. Heuristics have the potential for fast solutions but that could be far from the optimal solutions. In this paper, we show that it is possible to propose an optimal solution for the DARPTW, which can be transformed into an efficient heuristic with a data-oriented clustering-based approach. The heuristic can be used when dealing with a highly dynamic configuration and large-scale networks. We model the DARPTW with an integer linear programming model and design an algorithm based on the branch-and-bound method to solve the mathematical model exactly. Then we introduce a method that clusters the received requests at specified times and computes the exact solution for each cluster of requests. The results show that this method can significantly decrease the computation time while keeping the quality of the solution at a satisfactory level.