Learning and forgetting-based worker selection for tasks of varying complexity

Abstract

This paper presents an approach for selecting workers for tasks of varying complexity based on individual learning and forgetting characteristics in order to improve system productivity. The performance of a learning and forgetting-based selection (LFBS) policy is examined using simulation and compared to a baseline policy representing criteria used in practice. The effects of factors including worker redundancy and task-tenure on productivity are also examined in the environment of continuously staffed independent tasks. Results demonstrate that the LFBs policy significantly improves productivity relative to common practice and suggests that lower levels of redundancy and shorter task-tenures tend to mitigate some of the negative effects of forgetting.

Keywords:

• learning
• forgetting
• allocation
• production
• manpower planning
• scheduling
• simulation
• behaviour

Acknowledgements

We gratefully acknowledge a grant from the National Science Foundation SES-0217666 in support of this work.