Abstract
To address sustainable development issues of urban traffic, electric buses will join traditional bus system, and the scheduling of bus fleet should be adjusted due to the distinct features of electric buses. To this end, this paper develops a Multi-objective Bi-level programming model to collaboratively optimize the vehicle scheduling and charging scheduling of the mixed bus fleet under the operating conditions of a single depot. The upper level determines the vehicle scheduling to minimize the operating cost and carbon emissions under the constraints of connecting time between trips and the limited driving range of electric buses. The lower level is a charging scheduling problem that considers the charging time and the limited driving distance constraint to minimize the charging cost. The proposed model is solved with an integrated heuristic algorithm. The vehicle scheduling problem is addressed with the iterative neighborhood search algorithm based on simulated annealing, while the charging scheduling problem is solved with a greedy dynamic selection strategy based on the approach of multi-stage decision. Finally, case study is carried out based on a mixed bus fleet in Beijing, and the results validate the availability of the proposed model and solution algorithm.
Highlights
Because of the high energy conversion efficiency and low carbon emissions, electric bus has been continuously introduced to many public transit systems all over the world
Since single-depot vehicle scheduling problem (SDVSP) is the basis of vehicle scheduling, this study focuses on SDVSP
The results indicate that compared with shift and blocks, 2opt∗ has good performance in mixed bus operation, and the initial solution generation strategy has great influence on the final optimization results
Summary
Because of the high energy conversion efficiency and low carbon emissions, electric bus (eBus) has been continuously introduced to many public transit systems all over the world. The study of pure eBus fleet focuses on the modeling by considering limited driving ranges and long recharging duration of eBus. Reference [17] proposed a vehicle scheduling model for eBus with either battery replacement or fast charging, and developed the column-generation-based algorithm to solve the problem. (4) Considering the ‘‘Time-of-use Price’’ policy, the greedy dynamic selection strategy is designed to solve the charging scheduling problem of eBus. The algorithm can reduce the number of decision variables and the search range by precalculate the quantity of electricity that can charged in different price periods between trips.
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