Name
Abstract | ||
|---|---|---|
Work area optimization is a sub domain of Workforce optimization where the goal is the efficient utilization of resources (engineers) which leads to significant savings in operational costs and a corresponding increase in revenue. In this paper we present a Hybrid Genetic Algorithm where we will generate prior knowledge about the work area optimization problem using Deep Neural Network to provide good initial estimates to improve the performance of the Genetic Algorithm. The results show that the new approach provides faster convergence as well as more balanced WAs compared to a Conventional Genetic Algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CEEC.2018.8674236 | 2018 10th Computer Science and Electronic Engineering (CEEC) |
Keywords | Field | DocType |
Genetic algorithms,Optimization,Training,Sociology,Statistics,Mathematical model,Neural networks | Revenue,Convergence (routing),Mathematical optimization,Autoencoder,Workforce,Operational costs,Artificial neural network,Optimization problem,Genetic algorithm | Conference |
ISSN | ISBN | Citations |
2472-1530 | 978-1-5386-7275-4 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ravikiran Chimatapu | 1 | 1 | 0.36 |
| Hani Hagras | 2 | 1747 | 129.26 |
| Andrew Starkey | 3 | 22 | 3.61 |
| Gilbert Owusu | 4 | 102 | 22.66 |