Coordinated Scheduling of Mixed Bus Fleets and Drivers on a Single Route Considering Driving Habit Differences

As cities strive for cleaner and more efficient transit systems, electric buses (EBs) have emerged as a key component of public transportation decarbonization strategies. However, high battery costs and charging constraints hinder full electrification, leading transit operators to adopt mixed fleets comprising EBs and conventional diesel buses (CBs) as a transitional solution for the foreseeable future. These heterogeneous fleets pose new challenges for scheduling, particularly due to the differences in vehicle characteristics and driver familiarity with bus types. This study proposes a collaborative scheduling framework that integrates vehicle operational features and driver heterogeneity into a unified model for a single transit route. A mixed-integer linear programming (MILP) model is formulated to minimize operational costs while promoting workload equity among drivers. A tailored metaheuristic algorithm based on variable neighborhood tabu search (VNTS) is developed to solve the model efficiently. A case study on a real-world bus route in Dalian, China, demonstrates the effectiveness of the proposed approach in addressing both cost efficiency and fairness.