Automated resource planning through reinforcement learning.

BT is the UK's largest telecommunications company, and they employ over 20,000 engineers that work in the field. The engineers do jobs relating to television, internet and phone, and these jobs require different skills to complete. For BT, planning is very important in letting them have enough person-hours available to be able to complete all of the jobs that they have appointed, and also enough engineers with the required skills to do so. Planning the workforce entails, for example, deciding on how many hours of supply BT should make available for each type of job and assigning the engineers' hours to their different skills.
My project is concerned with these two aspects of planning. Due to the size of the problem at hand, it is naturally best to solve it automatically. The main approach we will use to help automate BT's planning process is reinforcement learning (RL). RL is a set of methodologies based on how humans and animals learn through reinforcement. For example, dogs learn to sit down on command by being given a treat when they sit, which acts as positive reinforcement. This is the general idea we aim to use; good planning actions will be rewarded and bad ones will be penalised. Over time, this allows us to learn which planning actions should be taken in which demand scenarios. This approach is not common in workforce planning, and so the research we do here will provide a novel, automatic and fast planning approach that will provide optimal plans for even the largest of workforces.

In partnership with BT.