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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Koole, G., Pot, A., Talim, J.: Routing heuristics for multi-skill call centers. In: Proceeding of the 2003 Simulation Conference, vol. 2, pp. 1813–1816 (2003)
Lin, A., Ahmad, A.: SilTerra’s experience in developing multi-skills technician. In: IEEE International Conference on Semiconductor Electronics, pp. 508–511 (2004)
Hu, Z., Mohd, R., Shboul, A.: The application of ant colony optimization technique (acot) for employees selection and training. In: First International Workshop on Database Technology and Applications, pp. 487–502 (2009)
Turchyn, O.: Comparative analysis of metaheuristics solving combinatorial optimization problems. In: 9th International Conference on the Experience of Designing and Applications of CAD Systems in Microelectronics, pp. 276–277 (2007)
Fanm, W., Gurmu, Z., Haile, E.: A bi-level metaheuristic approach to designing optimal bus transit route network. In: 3rd Annual International Conference on Cyber Technology in Automation, Control and Intelligent Systems, pp. 308–313 (2013)
Domberger, R., Frey, L., Hanne, T.: Single and multiobjective optimization of the train staff planning problem using genetic algorithms. In: IEEE Congress on Evolutionary Computation, pp. 970–977 (2008)
Liu, Y., Zhao, S., Du, X., Li, S.: Optimization of resource allocation in construction using genetic algorithms. In: Proceedings of the 2005 International Conference on Machine Learning, pp. 18–21 (2005)
Tanomaru, J.: Staff Scheduling by a Genetic Algorithm with Heuristic Operators. In: International Conference on Evolutionary Computation, pp. 456–461 (1995)
Alhanjouri, M., Alfarra, B.: Ant colony versus genetic algorithm based on travelling salesman problem. Int. J. Comput. Tech. Appl. 2(3), 570–578 (2013)
Hossain, K., El-Saleh, A., Ismail, M.: A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network. In: IEEE Student Conference on Research and Development, pp. 259–264 (2011)
Zhong, J., Hu, X., Gu, M., Zhang, J.: Comparison of performance between different selection strategies on simple genetic algorithms. In: Proceeding of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, pp. 1115–1121 (2005)
Murata, T., Ishibuchi, H.: Positive and negative combination effects if crossover and mutation operators in sequencing problems. In: Proceeding of the IEEE International Congress on Evolutionary Computation, pp. 170–175 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-25032-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25030-4
Online ISBN: 978-3-319-25032-8
eBook Packages: Computer ScienceComputer Science (R0)