The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs
