An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations

Çapar, İsmail Davut; Kuby, Michael; Leon, V. Jorge; Tsai, Yu-Jiun

doi:10.1016/j.ejor.2012.11.033

Cited by 183 publications

(138 citation statements)

References 33 publications

Supporting

Mentioning

138

Contrasting

Order By: Relevance

“…Observe that, similar to the study by Capar et al (2013), Constraints (9) and (10) can be replaced by the following constraints to handle the model more efficiently…”

Section: Model Logicmentioning

confidence: 99%

“…As previously mentioned, generation of these combinations require extensive amount of time, especially when the path is much longer with respect to the range of the vehicle. Capar et al (2013) presented a different modeling logic that reduces not only the preprocessing times but also the model solution times. Without generating the feasible combinations for each path, this new logic models the 'refuelability' of the arcs.…”

Section: Model Logicmentioning

confidence: 99%

“…Being a commonly used network in the literature (Capar et al, 2013;Hodgson, 1990;Kim & Kuby, 2012;Kuby & Lim, 2005;Lim & Kuby, 2010;MirHassani & Ebrazi, 2013;Wang & Wang, 2010), 25-node road network constitutes a suitable test bed for us to compare the performance of B&P with the benchmark studies in the literature. The CA road network on the other hand is a close representation of the actual California State road network and allows us to test B&P in realistic large problem instances.…”

Section: Datamentioning

confidence: 99%

“…In order to overcome this drawback, Lim and Kuby (2010) propose three heuristic algorithms: greedy-adding, greedy-adding with substitution and genetic algorithm. In a similar line of efforts, a different refueling logic is embedded into the MILP model by Capar et al (2013). The authors propose a simple, yet powerful formulation that solves the FRLM to optimality in a reasonable amount of time.…”

Section: Introductionmentioning

confidence: 99%

“…These combinations are given as input to a mixed integer linear programming (MILP) formulation in the second stage. In FRLM, at least a half-full tank of fuel is required at the final destination with no refueling station (Capar, Kuby, Leon, & Tsai, 2013;Kuby & Lim, 2005, 2007Kuby, Lines, Schultz, Xie, Kim, & Lim, 2009;MirHassani & Ebrazi, 2013). This enables the vehicle to have enough fuel to complete a round trip.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Yıldız

Arslan

Karaşan

2016

European Journal of Operational Research

101

View full text Add to dashboard Cite

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.

show abstract

“…Observe that, similar to the study by Capar et al (2013), Constraints (9) and (10) can be replaced by the following constraints to handle the model more efficiently…”

Section: Model Logicmentioning

confidence: 99%

Section: Model Logicmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Yıldız

Arslan

Karaşan

2016

European Journal of Operational Research

101

View full text Add to dashboard Cite

show abstract

A data-driven statistical approach for extending electric vehicle charging infrastructure

et al. 2018

View full text Add to dashboard Cite

Summary Current trends suggest that there is a substantial increase in the overall usage of electric vehicles (EVs). This, in turn, is causing drastic changes in the transportation industry and, more broadly, in business, policy making, and society. One concrete challenge brought by the increase in the number of EVs is a higher demand for charging stations. This paper presents a methodology to address the challenge of EV charging station deployment. The proposed methodology combines multiple sources of heterogeneous real‐world data for the sake of deriving insights that can be of a great value to decision makers in the field, such as EV charging infrastructure providers and/or local governments. Our starting point is the business data, ie, data describing charging infrastructure, historical data about charging transactions, and information about competitors in the market. Another type of data used are geographical data, such as places of interest located around chargers (eg, hospitals, restaurants, and shops) and driving distances between available chargers. The merged data from different sources are used to predict charging station utilization when EV charging infrastructure and/or contextual data change, eg, when another charging station or a place of interest is created. On the basis of such predictions, we suggest where to deploy new charging stations. We foresee that the proposed methodology can be used by EV charging infrastructure providers and/or local governments as a decision support tool that prescribes an optimal area to place a new charging station while keeping a desired level of utilization of the charging stations. We showcase the proposed methodology with an illustrative example involving the Dutch EV charging infrastructure through the period from 2013 to 2016. Specifically, we prescribe the optimal location for new ELaadNL charging stations based on different objectives such as maximizing the overall charging network utilization and/or increasing the number of chargers in scarcely populated areas.

show abstract

Range coverage location model: An optimization model for the charging station location problem in a transportation network to cover intercity travels

Yılmaz

Yağmahan

2021

Intl J of Energy Research

View full text Add to dashboard Cite

Summary Equipping highways with charging stations (CSs) is a fundamental step for travelling with electric vehicles (EVs) between cities and countries conveniently. This article focuses on locating CSs to fully connect roads that may require multiple charging events by considering the minimum driving range for all possible paths in a transportation network. For this purpose, we present a new binary integer linear programming model named the Range Coverage Location Model (RCLM) to find the minimum required CSs and their locations that the driving range can cover without defining the paths in the network. By adding the result of RCLM as a constraint to the model, the optimum locations that maximize the EV flows are determined with the RCLM‐Max model. Two versions of the RCLM are introduced. The link‐based RCLM is designed for problems in which there are CSs in each of the origin/destination (OD) nodes (intersections), while the network‐based RCLM aims to connect links without stopping by the OD nodes, making the model stricter but convenient for EV travels. The proposed models are validated through extensive computational experiments with real data from a highway network in Turkey. The experiments show that RCLM and RCLM‐Max can solve very large‐scale problems in a very short CPU time. The findings suggest that the link‐based RCLM can be applied when the budget is at the forefront, and the network‐based model is preferred if the aim is to connect the main roads without stopping by the OD nodes.

show abstract

An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations

Cited by 183 publications

References 33 publications

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

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

A data-driven statistical approach for extending electric vehicle charging infrastructure

Range coverage location model: An optimization model for the charging station location problem in a transportation network to cover intercity travels

Contact Info

Product

Resources

About