Abstract
In organisations with a large mobile workforce there is a need to improve the operational efficiency of the engineers who form the mobile workforce. This improvement can lead to significant savings in operational costs and a corresponding increase in revenue. The operational efficiency of the engineers can be improved by optimising the geographic areas within which the engineers operate. This process is known as Work Area Optimization and it is a subdomain of Workforce Optimization. In this paper, we will present a Hybrid Genetic Algorithm where we will use Deep Neural Networks to generate prior knowledge about the Work Area Optimization problem and use this knowledge to generate improved initial estimates which in turn improves the performance of an existing Genetic Algorithm that does Work Area Optimization. We will also compare our approach with prior knowledge generated with the help of human experts with years of experience in the field. We show that our new approach is as good as or better in generating the prior knowledge when compared to human experts.
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References
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Chimatapu, R., Hagras, H., Starkey, A.J., Owusu, G. (2018). Enhancing Human Decision Making for Workforce Optimisation Using a Stacked Auto Encoder Based Hybrid Genetic Algorithm. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_5
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