This thesis addresses the computational challenges posed by the Dial-a-Ride Problem (DARP), a vehicle routing problem characterized by pickup and drop-off requests subject to time windows and vehicle capacity constraints. This work investigates whether quantum-informed heuristics – leveraging quantum correlations derived from the Quantum Approximate Optimization Algorithm (QAOA) – can enhance the performance of classical algorithms in solving the DARP. To this end, we formulated a Mixed-Integer Quadratic Program (MIQP) capable of modeling time window constraints without continuous variables through novel penalty terms. The formulation was further translated into a Quadratic Unconstrained Binary Optimization (QUBO) model, enabling compatibility with quantum algorithms. Building upon this, we adapted the Quantum-Informed Recursive Optimization (QIRO) algorithm to the DARP and incorporated quantum-derived operators into a custom ALNS implementation. Experimental results demonstrate that the MIQP formulation effectively promotes time-window adherence, and the QIRO algorithm yields feasible solutions across problem sizes using shallow QAOA circuits. While the quantum-enhanced ALNS showed no significant improvement over the classical baseline, the quality of quantum correlations was identified as a limiting factor. These findings suggest that even with current hardware limitations, quantum-informed heuristics have potential to augment classical optimization techniques, particularly in domains like logistics, where partial quantum advantage could translate into operational efficiency. Further improvements in quantum devices or hybrid integration schemes may unlock tangible benefits for real-world transportation systems.