Joint Optimization of Regular Charging Electric Bus Transit Network Schedule and Stationary Charger Deployment considering Partial Charging Policy and Time-of-Use Electricity Prices

Li, Xinghua; Wang, Tianzuo; Li, Lingjie; Feng, Feiyu; Wang, Wei; CHENG, Cheng

doi:10.1155/2020/8863905

Cited by 22 publications

(13 citation statements)

References 39 publications

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“…e model was solved by the standard solver. Li et al [20] addressed EVSP along with the charger deployment problem. An adaptive genetic algorithm is designed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Electric Bus Scheduling under Travel Time Uncertainty: A Robust Model and Solution Method

Jiang

Zhang

2021

Journal of Advanced Transportation

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With the increasing adoption of electric buses (e-buses), e-bus scheduling problem has become an essential part of transit operation planning. As e-buses have a limited battery capacity, e-bus scheduling problem aims to assign vehicles to timetabled service trips on the bus routes considering their charging demand. Affected by the dynamic operation environment, the travel time and energy consumption of the e-buses often display considerable randomness, resulting in unexpected trip start delays and battery energy shortages. In this paper, we addressed the e-bus scheduling problem under travel time uncertainty by robust optimization approaches. We consider the cardinality constrained uncertainty set to formulate a robust multidepot EVSP model considering trip time uncertainty and partial recharging. The model is developed based on the dynamic programming equations that we formulated for trip chain robustness checking. A branch-and-price (BP) algorithm is devised to generate provably high-quality solutions for large-scale instances. In the BP algorithm, an efficient label setting algorithm is developed to solve the robust resource-constrained shortest path subproblem. Comprehensive numerical experiments are conducted based on the bus routes in Shenzhen to demonstrate the effectiveness of the suggested methodology. The robustness of the schedules was evaluated through Monte Carlo simulation. The results show that the trip start delay and battery energy shortage caused by the travel time uncertainty can be effectively reduced at the expense of an increase in the operational cost. A trade-off should be made between the reduction in infeasibility rate and increase in operational cost to choose a proper uncertainty budget.

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“…e model was solved by the standard solver. Li et al [20] addressed EVSP along with the charger deployment problem. An adaptive genetic algorithm is designed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Electric Bus Scheduling under Travel Time Uncertainty: A Robust Model and Solution Method

Jiang

Zhang

2021

Journal of Advanced Transportation

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“…Li et al (2020) addressed the EVSP in joint with the charger deployment problem. An adaptive GA was designed to solve the problem [25].…”

Section: Literature Review 21 E-bus Scheduling Problemmentioning

confidence: 99%

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

Jiang

Zhang

2021

Sustainability

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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.

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“…Note that the constraint is formulated as a logical expression that binary variables multiply together. Equation (12) ensures that a vehicle should be assigned to at least one place and can only be assigned to one place during the three-step recharging procedure. Equation (13) establishes a connection between two occupancies at one place, which is to ensure that the processing sequence at a place should be unique.…”

Section: Constraint Programming Of Idrrs Based On Fjspmentioning

confidence: 99%

“…Existing studies had integrated the recharging requests into the timetables to schedule the electric vehicles more reasonably according to recharging and energy consumption features [11]. Li et al proposed an integrated approach to planning the bus network scheduling considering the station chargers positioning [12]. Teng et al modeled a multiobjective optimization problem of integrated timetabling and vehicle scheduling considering vehicle recharging [13].…”

Section: Introductionmentioning

confidence: 99%

“…A subsequent problem is how to allocate chargers within the depot according to the required recharging plans. e method in [12] can assign charge depots for vehicles but cannot specifically allocate chargers. Although the article [13] generates schedules including trip periods and recharging periods, the allocation and occupancy scheduling of the charging devices in the depot is beyond their concerns.…”

Section: Introductionmentioning

confidence: 99%

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Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

Wang

Ren

et al. 2021

Journal of Advanced Transportation

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The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

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Joint Optimization of Regular Charging Electric Bus Transit Network Schedule and Stationary Charger Deployment considering Partial Charging Policy and Time-of-Use Electricity Prices

Cited by 22 publications

References 39 publications

Optimal Electric Bus Scheduling under Travel Time Uncertainty: A Robust Model and Solution Method

Optimal Electric Bus Scheduling under Travel Time Uncertainty: A Robust Model and Solution Method

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

Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

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