Model and Solution Methods for the Mixed-Fleet Multi-Terminal Bus Scheduling Problem

Picarelli, Erika; Rinaldi, Marco; D’Ariano, Andrea; Viti, Francesco

doi:10.1016/j.trpro.2020.03.099

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Sivagnanam et al (2020) introduced an integer program for optimal discretetime scheduling model to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging while serving an existing fixed-route transit schedule. Picarelli et al (2020) presented a mixed-integer linear programming model for the mixed bus fleet scheduling problem and implemented a time-based decomposition framework. This method could provide near-optimal solutions that explicitly considered the energy constraints arising from EB operations while establishing an advantageous trade-off between delaying trips to implement quick charging of EBs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Bie

Wang

et al. 2021

Computer aided Civil Eng

107

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This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption.First, a model for estimating the trip energy consumption is proposed based on field-collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Bie

Wang

et al. 2021

Computer aided Civil Eng

107

View full text Add to dashboard Cite

show abstract

“…Other researchers have applied approaches such as integer programming (Nageshrao, Jacob, and Wilkins (2017), Lotfi et al (2020), Picarelli et al (2020)), Markov decision processes (Wang et al (2018)), greedy algorithms (Jefferies and Göhlich (2020)), genetic algorithms (Gao et al (2018), Chao and Xiaohong (2013)), space-time networks (Olsen, Kliewer, and Wolbeck (2020)), and dynamic programming (Wang, Kang, and Liu (2020)) to optimally assign electric buses to charging stations. Jahic, Eskander, and Schulz (2019) use preemptive, quasipreemptive, and non-preemptive approaches to effectively handle the load in charging stations or garages.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Sivagnanam

Ayman

Wilbur

et al. 2021

AAAI

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Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO2 emission annually.

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“…[31,32], in preparation and support towards widespread Public Transport electrification. In this Section we present results stemming from our own recent research efforts [33][34][35] concerning the development of mixed-fleet vehicle scheduling models and algorithms tailored to the ongoing electrification of the bus fleet in the City of Luxembourg.…”

Section: Mixed Fleet Vehicle Scheduling and Charging Optimizationmentioning

confidence: 99%

Optimal Management of Electrified and Cooperative Bus Systems

Viti¹,

Rinaldi²,

Laskaris³

2021

Models and Technologies for Smart, Sustainable and Safe Transportation Systems

Self Cite

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This chapter presents an integrated management approach exploiting the potentials of the new Cooperative Intelligent Transportation Systems (C-ITS) to meet the requirements of the next generation Public Transport (PT). This approach considers the additional complexity of electrification—for instance electric busses need to periodically recharge during operation using dedicated infrastructure. This not only can impact service level, but also extend operating costs with complex electric charges. We develop new strategies explicitly optimizing the interactions within the PT ecosystem consisting of vehicles, traffic signals, and e-bus charging infrastructure. To achieve these goals, we rely on vehicle control rather than on the use of transit signal priority, which in congested urban scenarios can have negative effects on overall traffic performance. The main research challenges are in formulating and solving complex multi-objective optimization problems and real-time control. The proposed system is tested and evaluated in simulation showing the benefits of electrified and cooperative bus systems.

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Model and Solution Methods for the Mixed-Fleet Multi-Terminal Bus Scheduling Problem

Cited by 8 publications

References 10 publications

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Optimal Management of Electrified and Cooperative Bus Systems

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