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Fuel Efficient Truck Platooning with Time Restrictions and Multiple Speeds Solved by a Particle Swarm Optimisation

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Theory and Practice of Natural Computing (TPNC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11324))

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Abstract

In this paper, the problem of driving vehicles behind each other in close proximity as a file to reduce the total fuel consumption, called fuel-efficient platooning (FEP), is investigated. Some real-life attitudes like time restrictions and multiple allowable speeds for vehicles are taken into account. Linear mathematical formulations are presented for the FEP problem, which are coded in GAMS and solved by the GUROBI solver. Since the problem has a high computational complexity, an alternative evolutionary solution approach with Particle Swarm Optimisation (PSO) is proposed. An appropriate application procedure is given, which converts the continuous solution space of PSO into the routing, time scheduling and speed adjustment. The performance of our PSO is tested with some generated samples including up to 1000 vehicles on the graph of Chicago road network. The results verify the goodness of our PSO in terms of solution quality and time.

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Authors and Affiliations

  1. Department of Informatics, Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Abtin Nourmohammadzadeh & Sven Hartmann

Authors
  1. Abtin Nourmohammadzadeh
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  2. Sven Hartmann
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Corresponding author

Correspondence to Abtin Nourmohammadzadeh .

Editor information

Editors and Affiliations

  1. School of Business, University College Dublin, Dublin, Ireland

    David Fagan

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  4. Escuela Politécnica, University of Extremadura, Caceres, Spain

    Miguel A. Vega-Rodríguez

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Nourmohammadzadeh, A., Hartmann, S. (2018). Fuel Efficient Truck Platooning with Time Restrictions and Multiple Speeds Solved by a Particle Swarm Optimisation. In: Fagan, D., Martín-Vide, C., O'Neill, M., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-04070-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04069-7

  • Online ISBN: 978-3-030-04070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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