Electric vehicle fleet management using ant colony optimisation

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dc.contributor.authorMuriel, Javier Biera
dc.contributor.authorFotouhi, Abbas
dc.date.accessioned2020-02-14T12:07:47Z
dc.date.available2020-02-14T12:07:47Z
dc.date.issued2019-12-03
dc.description.abstractThis 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.identifier.citationBiera Muriel J, Fotouhi A. (2020) Electric vehicle fleet management using ant colony optimisation. International Journal of Strategic Engineering, Volume 3, Issue 1, 2020, Article number 1en_UK
dc.identifier.issn2572-4959
dc.identifier.urihttps://doi.org/10.4018/IJoSE.2020010101
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15132
dc.language.isoenen_UK
dc.publisherIGI Globalen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleElectric vehicle fleet management using ant colony optimisationen_UK
dc.typeArticleen_UK

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