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

TL;DR

This paper explores the use of Map-Elites for workforce scheduling and routing, comparing its effectiveness and diversity of solutions to traditional evolutionary algorithms under different computational budgets.

Contribution

It demonstrates that Map-Elites can outperform evolutionary algorithms in solution diversity and user insight at larger computational budgets for workforce scheduling problems.

Findings

Map-Elites provides more diverse solutions than EA at larger budgets.

EA performs better in solution quality with small computational budgets.

Map-Elites offers valuable insights and diverse options for end-users.

Abstract

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.