Abstract:

In order to address the issue of imbalanced task distribution between electric bus vehicles and drivers in the loop line, a joint optimal scheduling model is presented in this paper. It mainly allocates vehicles and drivers in the clockwise and counterclockwise directions to enhance the overall utilization rate. Given the circular route and the non-fixed passengers and vehicles, an orderly charging management plan and the vehicle and driver scheduling scheme are formulated with the aim of minimizing the total operating cost and schedule adjustment by comprehensively considering the constraints of vehicle mileage, workload, the number of charging piles, vehicle charging time, driver working time and rest time. Regarding the solution, the mixed integer nonlinear programming model is transformed into a linear programming model through linear transformation, and the scheduling scheme is obtained using the CPLEX solver. Secondly, the multi-objective particle swarm algorithm (MOPSO) and the improved multi-objective particle swarm algorithm (ε-MOPSO) based on the ε constraint processing mechanism are respectively employed to solve the scheduling scheme, and the convergence and uniformity of the external file set are ensured through the grid method. Finally, this paper takes Bus 200 (inner and outer rings) of Beijing Ring Line as an example to verify and compare the calculation results of the CPLEX solver, the traditional multi-objective particle swarm optimization (MOPSO), and the improved multi-objective particle swarm optimization (ε-MOPSO) based on the ε constraint processing mechanism proposed herein. The results validate the efficacy of the enhanced algorithm. The optimized scheduling plan reduces the number of vehicles from 28 to 23, amounting to a total of 17.86%. The number of drivers drops from 28 to 25, with a total reduction of 10.71%. The reduced fleet size and the number of drivers thereby lead to a decrease in total operating costs. The timetable is adjusted by an average of 4.13 minutes per departure time, and the departures are more evenly distributed to guarantee the demand of passengers. It enhances the operational efficiency of public transport and holds significant practical significance.

Key words: urban traffic, joint scheduling, multi-objective particle swarm optimization, loop electric bus;ε constraint