Optimisation and Illumination of a Real-World Workforce Scheduling and Routing Application (WSRP) via Map-Elites

Workforce Scheduling and Routing Problems (WSRP) are very common in many practical domains, and usually have a number of objectives of interest to the end-user. Illumination algorithms such as Map-Elites (ME) have recently gained traction in application to 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 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 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.