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Genetic Algorithm for Generation Multistage Tourist Route of Electrical Vehicle

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Computer Information Systems and Industrial Management (CISIM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12133))

Abstract

The problem of selection points of interest which tourist wants to visit the most in the case when tourist travels by electric vehicle (EV) is examined in this paper. Furthermore the battery capacity of EVs is limited so charging stations are selected to the route, in order to a tourist could recharge the battery and move on to the next stage of the route. The genetic algorithm is proposed and tests for the different limitations of EVs batteries are conducted on realistic database. The experimental results show that the proposed genetic algorithm can successfully be used in this context.

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Acknowledgements

The research was carried out as a part of research work nr WZ/WI/1/19 at Bialystok University of Technology financed from a subsidy of the Minister of Science and Higher Education.

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Authors and Affiliations

  1. Bialystok University of Technology, Wiejska Street 45A, 15-351, Bialystok, Poland

    Joanna Karbowska-Chilinska & Kacper Chociej

Authors
  1. Joanna Karbowska-Chilinska
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  2. Kacper Chociej
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Corresponding author

Correspondence to Joanna Karbowska-Chilinska .

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Editors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Jiří Dvorský

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Karbowska-Chilinska, J., Chociej, K. (2020). Genetic Algorithm for Generation Multistage Tourist Route of Electrical Vehicle. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_31

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  • DOI: https://doi.org/10.1007/978-3-030-47679-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47678-6

  • Online ISBN: 978-3-030-47679-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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