A Genetic Algorithm Based System for Simultaneous Optimisation of Workforce Skills and Teams

In large organisations with multi-skilled workforces, continued optimisation and adaptation of the skill sets of each of the engineers in the workforce are very important. However, this change in skill sets can have an impact on the engineer’s usefulness in any team. If an engineer has skills easily obtainable by others in the team, that particular engineer might be more useful in a neighbouring team where that skill may be scarce. A typical way to handle skilling and resource movement would be to perform them in isolation. This is a sub-optimal way of optimising the workforce overall, as there would be better combinations found if the effect of upskilling some of the workforce was also evaluated against the resultant move recommendations at the time the solutions are being evaluated. This paper presents a genetic algorithm-based system for the optimal selection of engineers to be upskilled and simultaneous suggestions of engineers who should swap teams. The results show that combining team moves and engineer upskilling in the same optimisation process lead to an increase in coverage across the region. The combined optimisation results produce better coverage than only moving engineers between teams, just upskilling the engineers and performing both these operations, but in isolation. Additionally one of the proposed methods was statistically significant in its level of improvement over current methods, achieving a p-value of 0.046. The developed system has been deployed in British Telecom’s (BT’s) iPatch optimisation system with improvements integrated from stakeholder feedback.