Abstract
In large organisations with multi-skilled workforces there is a need to optimise and adapt the skill set each member of the workforce has. Some of these engineers will show a natural ability to perform well with their current skill set and will show that they are ready to progress to a more advanced skill set. However other engineers will demonstrate that they do not have the natural aptitude to sufficiently complete the tasks they are set. In the first instance it may be beneficial to upskill the engineer, in the second instance there may be a need to remove that engineer from that particular workforce. In both these instances it is necessary to evaluate the impact of these changing skill sets on the performance on the organisation as a whole. This paper presents a genetic algorithm based system for the optimal selection of engineers to be upskilled. The data presented to the system was taken from a real world mobile workforce. The results showed that using this system to select employees for training has an overall increase in employee utilisation with a smaller percentage of the workforce being trained. The results show that the first few employees to be selected for training can produce the most benefit so selecting the right people is crucial.
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Starkey, A.J., Hagras, H., Shakya, S., Owusu, G. (2015). A Genetic Algorithm Based Approach for Workforce Upskilling. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_20
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DOI: https://doi.org/10.1007/978-3-319-25032-8_20
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