Day-to-Day Integrated Optimization of Bus Transit Maintenance and Vehicle Scheduling

This study addresses integrated bus maintenance and vehicle scheduling in a day-to-day dynamic operation setting to ensure that regular preventative maintenance and newly identified predictive maintenance needs are handled promptly. The problem is formulated as a mixed-integer linear programming aiming to minimize operational costs and risks over a day-to-day rolling planning horizon. A two-stage decomposition (TSD) method is proposed to solve this challenging problem. In the first stage, bus maintenance is scheduled on a daily basis. The second stage then optimizes specific bus maintenance and vehicle schedules within each day. With such a problem decomposition, the computational complexity is significantly reduced as the within-day problems become independent. Computational experiments conducted on a real-world bus line demonstrate the effectiveness and superiority of the TSD method. Compared to a one-step solution method and an algorithm in a similar existing study, the proposed TSD method consumes far less computation time, while delivering high-quality solutions. Moreover, the TSD solution method can be further greatly accelerated if the within-day problems of the second stage are solved in parallel with different computers. The computational results also highlight the benefits of the proposed two-stage optimization approach in enhancing operational cost-efficiency and bus resource utilization.