Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses
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 collaborati...
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doaj-2c974eeeb12141d2819c08a1f6f3dd702021-03-30T01:18:58ZengIEEEIEEE Access2169-35362020-01-0188056807210.1109/ACCESS.2020.29643918951145Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional BusesGuang-Jing Zhou0https://orcid.org/0000-0002-1461-3890Dong-Fan Xie1https://orcid.org/0000-0003-4495-8467Xiao-Mei Zhao2https://orcid.org/0000-0002-6443-4946Chaoru Lu3https://orcid.org/0000-0001-8418-7658School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaDepartment of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayTo 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.https://ieeexplore.ieee.org/document/8951145/Vehicle schedulingcharging schedulingmixed bus systemmulti-objective bi-level programmingcollaborative optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guang-Jing Zhou Dong-Fan Xie Xiao-Mei Zhao Chaoru Lu |
spellingShingle |
Guang-Jing Zhou Dong-Fan Xie Xiao-Mei Zhao Chaoru Lu Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses IEEE Access Vehicle scheduling charging scheduling mixed bus system multi-objective bi-level programming collaborative optimization |
author_facet |
Guang-Jing Zhou Dong-Fan Xie Xiao-Mei Zhao Chaoru Lu |
author_sort |
Guang-Jing Zhou |
title |
Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses |
title_short |
Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses |
title_full |
Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses |
title_fullStr |
Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses |
title_full_unstemmed |
Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses |
title_sort |
collaborative optimization of vehicle and charging scheduling for a bus fleet mixed with electric and traditional buses |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Vehicle scheduling charging scheduling mixed bus system multi-objective bi-level programming collaborative optimization |
url |
https://ieeexplore.ieee.org/document/8951145/ |
work_keys_str_mv |
AT guangjingzhou collaborativeoptimizationofvehicleandchargingschedulingforabusfleetmixedwithelectricandtraditionalbuses AT dongfanxie collaborativeoptimizationofvehicleandchargingschedulingforabusfleetmixedwithelectricandtraditionalbuses AT xiaomeizhao collaborativeoptimizationofvehicleandchargingschedulingforabusfleetmixedwithelectricandtraditionalbuses AT chaorulu collaborativeoptimizationofvehicleandchargingschedulingforabusfleetmixedwithelectricandtraditionalbuses |
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