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Explainable AI and Fuzzy Logic Systems Read chapter Read first chapter

2018 | OriginalPaper | Chapter

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

Authors : Abtin Nourmohammadzadeh, Sven Hartmann

Published in: Theory and Practice of Natural Computing

Publisher: Springer International Publishing

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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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previous chapter Landscape-Aware Constraint Handling Applied to Differential Evolution
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Metadata
Title
Fuel Efficient Truck Platooning with Time Restrictions and Multiple Speeds Solved by a Particle Swarm Optimisation
Authors
Abtin Nourmohammadzadeh
Sven Hartmann
Copyright Year
2018
Book
Theory and Practice of Natural Computing

Print ISBN: 978-3-030-04069-7

Electronic ISBN: 978-3-030-04070-3

Copyright Year: 2018

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

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