Electric vehicle fleet management using ant colony optimisation

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dc.contributor.author Muriel, Javier Biera
dc.contributor.author Fotouhi, Abbas
dc.date.accessioned 2020-02-14T12:07:47Z
dc.date.available 2020-02-14T12:07:47Z
dc.date.issued 2019-12-03
dc.identifier.citation Biera Muriel J & Fotouhi A. Electric vehicle fleet management using ant colony optimisation. International Journal of Strategic Engineering, Volume 3, Issue 1, 2020, pp.1-6 en_UK
dc.identifier.issn 2572-4959
dc.identifier.uri https://doi.org/10.4018/IJoSE.2020010101
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/15132
dc.description.abstract This research is focused on implementation of the ant colony optimisation (ACO) technique to solve an advanced version of the vehicle routing problem (VRP), called the fleet management system (FMS). An optimum solution of VRP can bring benefits for the fleet operators as well as contributing to the environment. Nowadays, particular considerations and modifications are needed to be applied in the existing FMS algorithms in response to the rapid growth of electric vehicles (EVs). For example, current FMS algorithms do not consider the limited range of EVs, their charging time or battery degradation. In this study, a new ACO-based FMS algorithm is developed for a fleet of EVs. A simulation platform is built in order to evaluate performance of the proposed FMS algorithm under different simulation case-studies. The simulation results are validated against a well-established method in the literature called nearest-neighbour technique. In each case-study, the overall mileage of the fleet is considered as an index to measure the performance of the FMS algorithm. en_UK
dc.language.iso en en_UK
dc.publisher IGI Global en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.title Electric vehicle fleet management using ant colony optimisation en_UK
dc.type Article en_UK

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