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JCR Impact Factor: 0.699
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2018 - 2

Efficient Placement of Electric Vehicles Charging Stations using Integer Linear Programming

MILJANIC, Z. See more information about MILJANIC, Z. on SCOPUS See more information about MILJANIC, Z. on IEEExplore See more information about MILJANIC, Z. on Web of Science, RADULOVIC, V. See more information about  RADULOVIC, V. on SCOPUS See more information about  RADULOVIC, V. on SCOPUS See more information about RADULOVIC, V. on Web of Science, LUTOVAC, B. See more information about LUTOVAC, B. on SCOPUS See more information about LUTOVAC, B. on SCOPUS See more information about LUTOVAC, B. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,294 KB) | Citation | Downloads: 387 | Views: 845

Author keywords
charging stations, electric vehicles, integer linear programming, optimization, path planning

References keywords
electric(21), vehicle(14), charging(13), optimal(11), vehicles(9), stations(9), research(9), transportation(8), planning(8), hybrid(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 11 - 16
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02002
Web of Science Accession Number: 000434245000002
SCOPUS ID: 85047873107

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This paper presents an efficient optimization approach for the placement of electric vehicles charging stations within the road network. The approach is based on the integer linear programming technique for solving optimization problems. In this paper, the optimization problem is formulated as complex combinatorial problem with goal to find minimum number of strategically selected locations for charging stations which will enable covering of the route between each two nodes of the road network. The necessary input data are the road network configuration with distances and adopted electric vehicle autonomy. The input data are used for creation of the graph representing the road infrastructure with nodes as potential locations for charging stations. The application of proposed approach is demonstrated on example road configuration with emphasis on its scalability, generality and processing cost.

References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]

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[CrossRef] [SCOPUS Times Cited 1]

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References Weight

Web of Science® Citations for all references: 1,089 TCR
SCOPUS® Citations for all references: 1,557 TCR

Web of Science® Average Citations per reference: 39 ACR
SCOPUS® Average Citations per reference: 56 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-05-21 18:48 in 147 seconds.

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Faculty of Electrical Engineering and Computer Science
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