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Workforce Scheduling and Routing Problems (WSRP) are very common in manypractical domains, and usually, have a number of objectives. Illuminationalgorithms such as Map-Elites (ME) have recently gained traction in applicationto {\em design} problems, in providing multiple diverse solutions as well asilluminating the solution space in terms of user-defined characteristics, buttypically require significant computational effort to produce the solutionarchive. We investigate whether ME can provide an effective approach to solvingWSRP, a {\em repetitive} problem in which solutions have to be produced quicklyand often. The goals of the paper are two-fold. The first is to evaluatewhether ME can provide solutions of competitive quality to an EvolutionaryAlgorithm (EA) in terms of a single objective function, and the second toexamine its ability to provide a repertoire of solutions that maximise userchoice. We find that very small computational budgets favour the EA in terms ofquality, but ME outperforms the EA at larger budgets, provides a more diversearray of solutions, and lends insight to the end-user.

Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites