Joint Optimization of Departure Times and Vehicle Types for a Multi-type Bus Fleet
Electric bus systems significantly differ from traditional fuel-powered buses in terms of range limitations,vehicle-type configurations,and operating economics.However,existing bus timetabling methods seldom consider the impacts of time-dependent passenger demand,uneven departure times,and vehicle-type configuration,making it difficult to balance the passenger travel experience with the operating cost of the bus system.This study focuses on an operational scenario involving a mixed fleet of electric buses with multiple vehicle types.To address the shortcomings of the existing two-stage optimization models in terms of flexibility and optimality,an integrated optimization method is proposed for bus departure times and vehicle configurations.The aim is to minimize the average passenger waiting time and bus operating costs by considering the fleet size required for the route.To achieve this,a multi-objective bus-timetable optimization model was established.Furthermore,the nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)with enhanced genetic operations was employed to solve the model and obtain the Pareto optimal solution set for the problem.Subsequently,timetable schemes were selected from this set that represented three different utility preferences:service first,equilibrium mode,and cost first.Finally,the proposed timetable optimization model and its solving algorithm were verified based on a real-life bus route to demonstrate their effectiveness and applicability.The results indicate that the proposed approach can generate timetable schemes that conform to passenger demand flows and represent various utility preferences.Compared with the two-stage models,the optimized solution in the equilibrium mode using the proposed method reduces the average passenger waiting time by 12.8％and decreases the total operating cost by 5.7％.This comprehensive improvement can enhance the quality of bus services while improving operating economics.
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