Optimisation and Illumination of a Real-world Workforce Scheduling and
  Routing Application via Map-Elites
  Workforce Scheduling and Routing Problems (WSRP) are very common in many
practical domains, and usually, have a number of objectives. Illumination
algorithms such as Map-Elites (ME) have recently gained traction in application
to {\em design} problems, in providing multiple diverse solutions as well as
illuminating the solution space in terms of user-defined characteristics, but
typically require significant computational effort to produce the solution
archive. We investigate whether ME can provide an effective approach to solving
WSRP, a {\em repetitive} problem in which solutions have to be produced quickly
and often. The goals of the paper are two-fold. The first is to evaluate
whether ME can provide solutions of competitive quality to an Evolutionary
Algorithm (EA) in terms of a single objective function, and the second to
examine its ability to provide a repertoire of solutions that maximise user
choice. We find that very small computational budgets favour the EA in terms of
quality, but ME outperforms the EA at larger budgets, provides a more diverse
array of solutions, and lends insight to the end-user.
