Multi-Depot Electric Bus Scheduling Considering Operational Constraint and Partial Charging: A Case Study in Shenzhen, China

被引:19
作者
Jiang, Mengyan [1 ]
Zhang, Yi [1 ,2 ,3 ]
机构
[1] Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Ctr Environm Sci & New Energy Technol, Shenzhen 518055, Peoples R China
[2] Tsinghua Univ, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing 100084, Peoples R China
[3] Tsinghua Univ, Shenzhen Int Grad Sch, Inst Future Human Habitats, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
electric bus; scheduling; Large Neighborhood Search; partial charging; multi-depot; vehicle relocation; VEHICLE-ROUTING PROBLEM; FLEET; STRATEGIES;
D O I
10.3390/su14010255
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Electric buses (e-buses) demonstrate great potential in improving urban air quality thanks to zero tailpipe emissions and thus being increasingly introduced to the public transportation systems. In the transit operation planning, a common requirement is that long-distance non-service travel of the buses among bus terminals should be avoided in the schedule as it is not cost-effective. In addition, e-buses should begin and end a day of operation at their base depots. Based on the unique route configurations in Shenzhen, the above two requirements add further constraint to the form of feasible schedules and make the e-bus scheduling problem more difficult. We call these two requirements the vehicle relocation constraint. This paper addresses a multi-depot e-bus scheduling problem considering the vehicle relocation constraint and partial charging. A mixed integer programming model is formulated with the aim to minimize the operational cost. A Large Neighborhood Search (LNS) heuristic is devised with novel destroy-and-repair operators to tackle the vehicle relocation constraint. Numerical experiments are conducted based on multi-route operation cases in Shenzhen to verify the model and effectiveness of the LNS heuristic. A few insights are derived on the decision of battery capacity, charging rate and deployment of the charging infrastructure.
引用
收藏
页数:20
相关论文
共 32 条
[1]  
[Anonymous], 2020, BNEF
[2]   ON SOME MATCHING PROBLEMS ARISING IN VEHICLE SCHEDULING MODELS [J].
BERTOSSI, AA ;
CARRARESI, P ;
GALLO, G .
NETWORKS, 1987, 17 (03) :271-281
[3]   Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption [J].
Bie, Yiming ;
Ji, Jinhua ;
Wang, Xiangyu ;
Qu, Xiaobo .
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2021, 36 (12) :1530-1548
[4]   Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies [J].
Bie, Yiming ;
Hao, Mingjie ;
Guo, Mengzhu .
SUSTAINABILITY, 2021, 13 (04) :1-21
[5]  
Editorial, 2020, FOC EL CONS SUM UP S
[6]   Adjustments of public transit operations planning process for the use of electric buses [J].
Hall, Carl H. ;
Ceder, Avishai ;
Ekstrom, Joakim ;
Quttineh, Nils-Hassan .
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 23 (03) :216-230
[7]   Exact approach to the electric bus fleet scheduling [J].
Janovec, Maros ;
Kohani, Michal .
13TH INTERNATIONAL SCIENTIFIC CONFERENCE ON SUSTAINABLE, MODERN AND SAFE TRANSPORT (TRANSCOM 2019), 2019, 40 :1380-1387
[8]   Partial recharge strategies for the electric vehicle routing problem with time windows [J].
Keskin, Merve ;
Catay, Bulent .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 65 :111-127
[9]   The electric vehicle routing problem with shared charging stations [J].
Koc, Cagri ;
Jabali, Ola ;
Mendoza, Jorge E. ;
Laporte, Gilbert .
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, 2019, 26 (04) :1211-1243
[10]   Mixed bus fleet scheduling under range and refueling constraints [J].
Li, Lu ;
Lo, Hong K. ;
Xiao, Feng .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 104 :443-462