Artificial Intelligence-based Optimization Models for the Technical Workforce Allocation and Routing Problem Considering Productivity

Ensuring the reliability and availability of electric power networks is essential due to the increasing demands. An effective preventive maintenance strategy requires efficient resources allocation to perform the maintenance tasks, particularly the technical workforce. This paper introduces an innovative artificial intelligence-based approach to predict workforce productivity, aiming to optimize both the allocation of the technical workforce for maintenance tasks and their routing. In this study, two mathematical optimization models are introduced that utilize the output value of Artificial Neural Networks (ANN) for optimal resource allocation and routing. The first model focuses on team formation, considering the predicted productivity in order to ensure effective collaboration. While the second model focuses on the optimal assignment and routing of these teams to specific maintenance tasks. Validated with real-world data, the models show considerable promise in enhancing resource allocation, task assignment, and cost-efficiency in the electricity industry. Furthermore, sensitivity analysis has been conducted and managerial insights has been explored. The study also paves the way for future research, highlighting the potential for refining these models for more extensive applications.

@article{Alzeraif2023,
  title     = {Artificial Intelligence-based Optimization Models for the Technical Workforce Allocation and Routing Problem Considering Productivity},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Mariam Alzeraif and Ali Cheaitou},
  doi       = {10.14569/IJACSA.2023.0141233},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141233}
}