A network transformation heuristic approach for the deviation flow refueling location model

Kim, Jong Geun; Kuby, Michael

doi:10.1016/j.cor.2012.10.021

Cited by 88 publications

(40 citation statements)

References 23 publications

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“…Furthermore, the deviation-flow refueling model proposed by Kim and Kuby (2012) allows for the incorporation of deviations from the shortest paths. Additional publications dealing with the incorporation of deviation paths were presented by Huang et al (2015) and Kim and Kuby (2013). In addition, Capar et al (2013) presented a more efficient formulation of the FRLM, the arc cover path-cover FRLM (AC-PC FRLM).…”

Section: Literature Reviewmentioning

confidence: 98%

Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium

Riemann¹,

Wang

Busch

2015

Transportation Research Part C: Emerging Technologies

231

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Section: Literature Reviewmentioning

confidence: 98%

Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium

Riemann¹,

Wang

Busch

2015

Transportation Research Part C: Emerging Technologies

231

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“…Kim and Kuby (2012) relaxed FRLM to consider drivers' deviations as well as vehicles' limited driving range. Kim and Kuby (2013) presented a heuristic approach to solve the deviation flow refueling location model.…”

Section: Introductionmentioning

confidence: 99%

Selecting optimal location for electric recharging stations with queue

Hosseini

MirHassani

2015

KSCE J Civ Eng

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Dominance of fossil fuels in the transportation sector has led to environmental and economical problems and transition to Alternative Fuel Vehicle (AFV) has become a necessity. Electric Vehicle (EV) is one of the AFVs that deliver clear benefit of zero tailpipe emissions. The EVs have a limited range and their use is influenced by the location of recharging facilities. The charging time of EV is long and recharging in stations take a lot of time. In addition, the capacity of recharging stations is limited and if the capacity is occupied the vehicles must wait in queues. In this paper, a recharging station location model with queue is proposed which considers capacity, recharging time, and waiting time. The presented model is a set covering and can easily become a maximum cover model with or without predefined activities. A heuristic algorithm to solve the model is proposed and the model is evaluated using two networks under various conditions; a random generated and a network of Penghu County. The obtained results indicate that the model is able to identify the location and the number of the recharging stations and provide a good travel plan especially for tourism.

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“…In particular, Kim and Kuby [20] proposed the deviation-flow refueling location model (DFRLM), which is an extension of the FRLM considering driver deviation behavior when searching for potential sites for the refueling stations. Later, Kim and Kuby [24] suggested two heuristics to solve the DFRLM efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

A Continuous Deviation-Flow Location Problem for an Alternative-Fuel Refueling Station on a Tree-Like Transportation Network

Kweon

Hwang

Ventura

2017

Journal of Advanced Transportation

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Due to the increasing popularity of alternative-fuel (AF) vehicles in the last two decades, several models and solution techniques have been recently published in the literature to solve AF refueling station location problems. These problems can be classified depending on the set of candidate sites: when a (finite) set of candidate sites is predetermined, the problem is called discrete; when stations can be located anywhere along the network, the problem is called continuous. Most researchers have focused on the discrete version of the problem, but solutions to the discrete version are suboptimal to its continuous counterpart. This study addresses the continuous version of the problem for an AF refueling station on a tree-type transportation network when a portion of drivers are willing to deviate from their preplanned simple paths to receive refueling service. A polynomial time solution approach is proposed to solve the problem. We first present a new algorithm that identifies all possible deviation options for each travel path. Then, an efficient algorithm is used to determine the set of optimal locations for the refueling station that maximizes the total traffic flow covered. A numerical example is solved to illustrate the proposed solution approach.

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A network transformation heuristic approach for the deviation flow refueling location model

Cited by 88 publications

References 23 publications

Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium

Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium

Selecting optimal location for electric recharging stations with queue

A Continuous Deviation-Flow Location Problem for an Alternative-Fuel Refueling Station on a Tree-Like Transportation Network

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