Stochastic dynamic itinerary interception refueling location problem with queue delay for electric taxi charging stations

Jung, Jason J.; Chow, Joseph Y.J.; Jayakrishnan, R.; Park, Ji Young

doi:10.1016/j.trc.2014.01.008

Cited by 206 publications

(83 citation statements)

References 55 publications

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“…The model uses a probabilistic distribution function of the remaining fuel range. Jung et al [19] employed a bi-level stochastic queuing model to locate charging stations for electric taxis. Long et al [20] proposed a graph theoretic model based on demand and supply, that minimizes the total cost (investment and operation).…”

Section: Literature Reviewmentioning

confidence: 99%

Electric vehicles charging infrastructure location: a genetic algorithm approach

Efthymiou

Chrysostomou

Morfoulaki

et al. 2017

Eur. Transp. Res. Rev.

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Introduction As part of the overall goal of carbon emissions reduction, European cities are expected to encourage the electrification of urban transport. In order to prepare themselves to welcome the increased number of electric vehicles circulating in the city networks in the near future, they are expected to deploy networks of public electric vehicle chargers. The Electric Vehicle Charging Infrastructure Location Problem is an optimization problem that can be approached by linear programming, multi-objective optimization and genetic algorithms. Methods In the present paper, a genetic algorithm approach is presented. Since data from electric vehicles usage are still scarce, origin -destination data of conventional vehicles are used and the necessary assumptions to predict electric vehicles' penetration in the years to come are made. The algorithm and a user-friendly tool have been developed in R and tested for the city of Thessaloniki. ResultsThe results indicate that 15 stations would be required to cover 80% of the estimated electric vehicles charging demand in 2020 in the city of Thessaloniki and their optimal locations to install them are identified. Conclusions The tool that has been developed based on the genetic algorithm, is open source and freely available to interested users. The approach can be used to allocate charging stations at high-level, i.e. to zones, and the authors encourage its use by local authorities of other cities too, in Greece and worldwide, in order to deploy a plan for installing adequate charging infrastructure to cover future electric vehicles charging demand and reduce the electric vehicle Bdriver anxiety( i.e. the driver's concern of running out of battery) encouraging the widespread adoption of electromobility.

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Section: Literature Reviewmentioning

confidence: 99%

Electric vehicles charging infrastructure location: a genetic algorithm approach

Efthymiou

Chrysostomou

Morfoulaki

et al. 2017

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…In its current form, our methodology does not accommodate for the capacities of the refueling stations which is in the scope of recent works such as Upchurch, Kuby, and Lim (2009a) and Jung et al (2014). Driver's different preferences such as stopping tolerance can also be accommodated into this framework and the number of refueling stops on a given path might also be limited by a constant.…”

Section: Discussionmentioning

confidence: 99%

“…The presented example oversees capacity issues related to stations. Capacitated refueling stations are within the scope of recent studies such as Upchurch, Kuby, and Lim (2009b) and Jung, Chow, Jayakrishnan, and Park (2014). Though not within our scope, the flexibility provided by non-simple paths might prove useful in capacitated networks as well.…”

Section: Introductionmentioning

confidence: 99%

A branch and price approach for routing and refueling station location model

Yıldız

Arslan

Karaşan

2016

European Journal of Operational Research

104

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a b s t r a c tThe deviation flow refueling location problem is to locate p refueling stations in order to maximize the flow volume that can be refueled respecting the range limitations of the alternative fuel vehicles and the shortest path deviation tolerances of the drivers. We first provide an enhanced compact model based on a combination of existing models in the literature for this relatively new operations research problem. We then extend this problem and introduce the refueling station location problem which adds the routing aspect of the individual drivers. Our proposed branch and price algorithm relaxes the simple path assumption generally adopted in the existing studies and implicitly takes into account deviation tolerances without the pregeneration of the routes. Therefore, the decrease in solution times with respect to existing models is significant and our algorithm scales very efficiently to more realistic network dimensions.

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“…Xi et al [29] developed a simulationoptimization model that determined where to locate EV charging stations to maximize their use by privately owned EVs. Jung et al [30] reported a simulation-optimization location model including an upper level multiple-server allocation model with queueing delay and a lower level dispatch simulation and provided a solution algorithm that featured itinerary-interception, stochastic demand, and queueing delay. Dong et al [31] analyzed the impact of public charging station deployment on increasing electric miles traveled.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

Jing

Ramezani

et al. 2017

Journal of Advanced Transportation

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Location of public charging stations, range limit, and long battery-charging time inevitably affect drivers' path choice behavior and equilibrium flows of battery electric vehicles (BEVs) in a transportation network. This study investigates the effect of the location of BEVs public charging facilities on a network with mixed conventional gasoline vehicles (GVs) and BEVs. These two types of vehicles are distinguished from each other in terms of travel cost composition and distance limit. A bilevel model is developed to address this problem. In the upper level, the objective is to maximize coverage of BEV flows by locating a given number of charging stations on road segments considering budget constraints. A mixed-integer nonlinear program is proposed to formulate this model. A simple equilibrium-based heuristic algorithm is developed to obtain the solution. Finally, two numerical tests are presented to demonstrate applicability of the proposed model and feasibility and effectiveness of the solution algorithm. The results demonstrate that the equilibrium traffic flows are affected by charging speed, range limit, and charging facilities' utility and that BEV drivers incline to choose the route with charging stations and less charging time.

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Stochastic dynamic itinerary interception refueling location problem with queue delay for electric taxi charging stations

Cited by 206 publications

References 55 publications

Electric vehicles charging infrastructure location: a genetic algorithm approach

Electric vehicles charging infrastructure location: a genetic algorithm approach

A branch and price approach for routing and refueling station location model

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

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