A heuristic algorithm for optimal location of flow‐refueling capacitated stations

Hosseini, Meysam; MirHassani, S. A.

doi:10.1111/itor.12209

Cited by 29 publications

(22 citation statements)

References 25 publications

(57 reference statements)

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“…Limited charging station capacities were considered e.g. in [45] and [19]. The possibility of using paths slightly deviating from the shortest origin-destination paths was studied by Kim and Kuby [27], [28], Yildiz et al [52] and Li et al [31].…”

Section: Modeling the Ev Station Location Problemmentioning

confidence: 99%

“…Lee and Han [30] formulated the flow refueling location problem under a stochastic driving range as a MINLP and proposed a Benders-and-Price algorithm (BD&P) combining Benders decomposition and column generation techniques to solve their problem. Hosseini and MirHassani [19] investigated the problem with capacitated refueling stations and developed a heuristic solution method based on the Lagrangian relaxation (LGR) of the capacity constraints.…”

Section: Solving the Flow Refueling Location Problemmentioning

confidence: 99%

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A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

Kadri

Perrouault

Boujelben

et al. 2020

Computers & Operations Research

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Electric vehicles (EVs) represent one of the promising solutions to face environmental and energy concerns in transportation. Due to the limited range of EVs, deploying a charging infrastructure enabling EV drivers to carry out long distance trips is a key step to foster the widespread adoption of EVs. In this paper, we study the problem of locating EV fast charging stations so as to satisfy as much recharging demand as possible within the available investment budget. We focus on incorporating two important features into the optimization problem modeling: a multi-period decision making horizon and uncertainties on the recharging demand in terms of both the number of EVs to recharge and the set of long-distance trips to cover. Our objective is to determine the charging stations to be opened at each time period so as to maximize the expected value of the satisfied recharging demand over the entire planning horizon. To model the problem, we propose a multi-stage stochastic integer programming approach based on the use of a scenario tree to represent the uncertainties on the recharging demand. To solve the resulting large-size integer linear program, we develop two solution algorithms: an exact solution method based on a Benders decomposition and a heuristic approach based on a genetic algorithm. Our numerical results show that both methods perform well as compared to a standalone mathematical programming solver. Moreover, we provide the results of additional simulation experiments showing the practical benefit of the proposed multi-stage stochastic programming model as compared to a simpler multi-period deterministic model.

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Section: Modeling the Ev Station Location Problemmentioning

confidence: 99%

Section: Solving the Flow Refueling Location Problemmentioning

confidence: 99%

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

Kadri

Perrouault

Boujelben

et al. 2020

Computers & Operations Research

View full text Add to dashboard Cite

show abstract

“…Extensions of the basic problem taking into account a limited charging capacity of the stations (see e.g. [8] and [21]) or the possibility of using paths slightly deviating from the shortest origin-destination path (see e.g. [10], [14], [18] and [25]) were also considered.…”

Section: Position In the Literaturementioning

confidence: 99%

“…Constraints (16) state that if a trip q has a strictly positive probability of coverage (z q > 0), for each node l ∈ N q \ {O q }, there must exist a node k visited before l during trip q, where the vehicle can be refueled up to node l. Constraints (17) link the coverage probability variables z q to the binary variables w kl q . They state that variable z q is calculated as the smallest coverage probability over all segments [k, l] where node l is refueled by a station at node k. Constraints (3), (5), (6) and (8) are maintained from the deterministic formulation FRLM2.…”

Section: Expected Flow Refueling Location Model (Efrlm)mentioning

confidence: 99%

Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Boujelben

Gicquel

2019

Computers & Operations Research

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We seek to determine the best locations for electric vehicle fast charging stations under driving range uncertainty. Two stochastic programming based models have been recently proposed to handle the resulting stochastic flow refueling location problem: a first one maximizing the expected flow coverage of the network, a second one based on joint chance constraints. However, significant computational difficulties were encountered while solving large-size instances. We thus propose two efficient solution approaches for this problem. The first one is based on a new location-allocation type model for this problem and results in a MILP formulation, while the second one is a tabu search heuristic. Our numerical experiments show that when using the proposed MILP formulation, the computation time needed to provide guaranteed optimal solutions is significantly reduced as compared to the one needed when using the previously published MILP formulation. Moreover, our results also show that the tabu search method consistently provides good quality solutions within short computation times.

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“…This indicates that timely refueling on the road is one of the most fundamental reasonable location problem of gas stations, the core of which is the construction and evaluation of gas station location models, which are typically analyzed and evaluated based on Geography Information System (GIS) spatial analysis and the actual demand for vehicle refueling [18]. For example, based on different route choices and traveling conditions, Hosseini [19] and Miralinaghi [20] studied the construction methods of a gas station location model, as well as its heuristic algorithm, and finally showed that GIS spatial analysis can be used to scientifically optimize the location of gas stations. In Field (3), the major research is concerned with scientific scheduling and routing optimization for mobile refueling facilities, such as airport tankers, space refueling aircraft, etc.…”

Section: Motivationmentioning

confidence: 99%

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

Yang

Peng

et al. 2018

IJGI

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Abstract:Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

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A heuristic algorithm for optimal location of flow‐refueling capacitated stations

Cited by 29 publications

References 25 publications

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

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